Overview

Dataset statistics

Number of variables58
Number of observations1460
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory661.7 KiB
Average record size in memory464.1 B

Variable types

Numeric26
Categorical31
Boolean1

Alerts

MSSubClass is highly overall correlated with BldgType and 2 other fieldsHigh correlation
OverallQual is highly overall correlated with GrLivArea and 6 other fieldsHigh correlation
BsmtFinSF1 is highly overall correlated with BsmtUnfSFHigh correlation
BsmtUnfSF is highly overall correlated with BsmtFinSF1High correlation
TotalBsmtSF is highly overall correlated with 1stFlrSF and 1 other fieldsHigh correlation
1stFlrSF is highly overall correlated with TotalBsmtSF and 1 other fieldsHigh correlation
2ndFlrSF is highly overall correlated with GrLivArea and 2 other fieldsHigh correlation
GrLivArea is highly overall correlated with OverallQual and 4 other fieldsHigh correlation
BedroomAbvGr is highly overall correlated with 2ndFlrSF and 2 other fieldsHigh correlation
TotRmsAbvGrd is highly overall correlated with 2ndFlrSF and 3 other fieldsHigh correlation
GarageArea is highly overall correlated with OverallQual and 3 other fieldsHigh correlation
SalePrice is highly overall correlated with OverallQual and 7 other fieldsHigh correlation
Built_to_Sale is highly overall correlated with OverallQual and 4 other fieldsHigh correlation
Years_Since_Remod is highly overall correlated with OverallQual and 2 other fieldsHigh correlation
MSZoning is highly overall correlated with NeighborhoodHigh correlation
Neighborhood is highly overall correlated with MSZoningHigh correlation
BldgType is highly overall correlated with MSSubClassHigh correlation
HouseStyle is highly overall correlated with MSSubClassHigh correlation
Exterior1st is highly overall correlated with Exterior2ndHigh correlation
Exterior2nd is highly overall correlated with Exterior1stHigh correlation
ExterQual is highly overall correlated with OverallQual and 1 other fieldsHigh correlation
Foundation is highly overall correlated with Built_to_SaleHigh correlation
HalfBath is highly overall correlated with MSSubClassHigh correlation
KitchenQual is highly overall correlated with OverallQual and 1 other fieldsHigh correlation
GarageCars is highly overall correlated with GarageAreaHigh correlation
MSZoning is highly imbalanced (56.9%)Imbalance
Street is highly imbalanced (96.2%)Imbalance
LandContour is highly imbalanced (68.3%)Imbalance
Utilities is highly imbalanced (99.2%)Imbalance
LandSlope is highly imbalanced (78.8%)Imbalance
Condition1 is highly imbalanced (71.7%)Imbalance
Condition2 is highly imbalanced (96.4%)Imbalance
BldgType is highly imbalanced (59.4%)Imbalance
RoofStyle is highly imbalanced (65.1%)Imbalance
RoofMatl is highly imbalanced (94.4%)Imbalance
ExterCond is highly imbalanced (72.8%)Imbalance
Heating is highly imbalanced (92.7%)Imbalance
CentralAir is highly imbalanced (65.3%)Imbalance
BsmtHalfBath is highly imbalanced (79.7%)Imbalance
KitchenAbvGr is highly imbalanced (85.7%)Imbalance
Functional is highly imbalanced (81.9%)Imbalance
PavedDrive is highly imbalanced (69.9%)Imbalance
MiscVal is highly skewed (γ1 = 24.47679419)Skewed
MasVnrArea has 869 (59.5%) zerosZeros
BsmtFinSF1 has 467 (32.0%) zerosZeros
BsmtFinSF2 has 1293 (88.6%) zerosZeros
BsmtUnfSF has 118 (8.1%) zerosZeros
TotalBsmtSF has 37 (2.5%) zerosZeros
2ndFlrSF has 829 (56.8%) zerosZeros
LowQualFinSF has 1434 (98.2%) zerosZeros
GarageArea has 81 (5.5%) zerosZeros
WoodDeckSF has 761 (52.1%) zerosZeros
OpenPorchSF has 656 (44.9%) zerosZeros
EnclosedPorch has 1252 (85.8%) zerosZeros
3SsnPorch has 1436 (98.4%) zerosZeros
ScreenPorch has 1344 (92.1%) zerosZeros
PoolArea has 1453 (99.5%) zerosZeros
MiscVal has 1408 (96.4%) zerosZeros
Built_to_Sale has 64 (4.4%) zerosZeros
Years_Since_Remod has 125 (8.6%) zerosZeros

Reproduction

Analysis started2023-02-02 21:31:55.340344
Analysis finished2023-02-02 21:33:03.250842
Duration1 minute and 7.91 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

MSSubClass
Real number (ℝ)

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56.89726
Minimum20
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:03.313377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile20
Q120
median50
Q370
95-th percentile160
Maximum190
Range170
Interquartile range (IQR)50

Descriptive statistics

Standard deviation42.300571
Coefficient of variation (CV)0.74345532
Kurtosis1.580188
Mean56.89726
Median Absolute Deviation (MAD)30
Skewness1.4076567
Sum83070
Variance1789.3383
MonotonicityNot monotonic
2023-02-02T13:33:03.391341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
20 536
36.7%
60 299
20.5%
50 144
 
9.9%
120 87
 
6.0%
30 69
 
4.7%
160 63
 
4.3%
70 60
 
4.1%
80 58
 
4.0%
90 52
 
3.6%
190 30
 
2.1%
Other values (5) 62
 
4.2%
ValueCountFrequency (%)
20 536
36.7%
30 69
 
4.7%
40 4
 
0.3%
45 12
 
0.8%
50 144
 
9.9%
60 299
20.5%
70 60
 
4.1%
75 16
 
1.1%
80 58
 
4.0%
85 20
 
1.4%
ValueCountFrequency (%)
190 30
 
2.1%
180 10
 
0.7%
160 63
 
4.3%
120 87
 
6.0%
90 52
 
3.6%
85 20
 
1.4%
80 58
 
4.0%
75 16
 
1.1%
70 60
 
4.1%
60 299
20.5%

MSZoning
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
RL
1151 
RM
218 
FV
 
65
RH
 
16
C (all)
 
10

Length

Max length7
Median length2
Mean length2.0342466
Min length2

Characters and Unicode

Total characters2970
Distinct characters12
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRL
2nd rowRL
3rd rowRL
4th rowRL
5th rowRL

Common Values

ValueCountFrequency (%)
RL 1151
78.8%
RM 218
 
14.9%
FV 65
 
4.5%
RH 16
 
1.1%
C (all) 10
 
0.7%

Length

2023-02-02T13:33:03.483339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:03.582532image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
rl 1151
78.3%
rm 218
 
14.8%
fv 65
 
4.4%
rh 16
 
1.1%
c 10
 
0.7%
all 10
 
0.7%

Most occurring characters

ValueCountFrequency (%)
R 1385
46.6%
L 1151
38.8%
M 218
 
7.3%
F 65
 
2.2%
V 65
 
2.2%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
10
 
0.3%
( 10
 
0.3%
Other values (2) 20
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2910
98.0%
Lowercase Letter 30
 
1.0%
Space Separator 10
 
0.3%
Open Punctuation 10
 
0.3%
Close Punctuation 10
 
0.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 1385
47.6%
L 1151
39.6%
M 218
 
7.5%
F 65
 
2.2%
V 65
 
2.2%
H 16
 
0.5%
C 10
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
l 20
66.7%
a 10
33.3%
Space Separator
ValueCountFrequency (%)
10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2940
99.0%
Common 30
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 1385
47.1%
L 1151
39.1%
M 218
 
7.4%
F 65
 
2.2%
V 65
 
2.2%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
a 10
 
0.3%
Common
ValueCountFrequency (%)
10
33.3%
( 10
33.3%
) 10
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2970
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 1385
46.6%
L 1151
38.8%
M 218
 
7.3%
F 65
 
2.2%
V 65
 
2.2%
l 20
 
0.7%
H 16
 
0.5%
C 10
 
0.3%
10
 
0.3%
( 10
 
0.3%
Other values (2) 20
 
0.7%

LotArea
Real number (ℝ)

Distinct1073
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10516.828
Minimum1300
Maximum215245
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:03.668588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile3311.7
Q17553.5
median9478.5
Q311601.5
95-th percentile17401.15
Maximum215245
Range213945
Interquartile range (IQR)4048

Descriptive statistics

Standard deviation9981.2649
Coefficient of variation (CV)0.9490756
Kurtosis203.24327
Mean10516.828
Median Absolute Deviation (MAD)1998
Skewness12.207688
Sum15354569
Variance99625650
MonotonicityNot monotonic
2023-02-02T13:33:03.764596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7200 25
 
1.7%
9600 24
 
1.6%
6000 17
 
1.2%
9000 14
 
1.0%
8400 14
 
1.0%
10800 14
 
1.0%
1680 10
 
0.7%
7500 9
 
0.6%
9100 8
 
0.5%
8125 8
 
0.5%
Other values (1063) 1317
90.2%
ValueCountFrequency (%)
1300 1
 
0.1%
1477 1
 
0.1%
1491 1
 
0.1%
1526 1
 
0.1%
1533 2
 
0.1%
1596 1
 
0.1%
1680 10
0.7%
1869 1
 
0.1%
1890 2
 
0.1%
1920 1
 
0.1%
ValueCountFrequency (%)
215245 1
0.1%
164660 1
0.1%
159000 1
0.1%
115149 1
0.1%
70761 1
0.1%
63887 1
0.1%
57200 1
0.1%
53504 1
0.1%
53227 1
0.1%
53107 1
0.1%

Street
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Pave
1454 
Grvl
 
6

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5840
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPave
2nd rowPave
3rd rowPave
4th rowPave
5th rowPave

Common Values

ValueCountFrequency (%)
Pave 1454
99.6%
Grvl 6
 
0.4%

Length

2023-02-02T13:33:03.857124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:03.942823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
pave 1454
99.6%
grvl 6
 
0.4%

Most occurring characters

ValueCountFrequency (%)
v 1460
25.0%
P 1454
24.9%
a 1454
24.9%
e 1454
24.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4380
75.0%
Uppercase Letter 1460
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
v 1460
33.3%
a 1454
33.2%
e 1454
33.2%
r 6
 
0.1%
l 6
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
P 1454
99.6%
G 6
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 5840
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
v 1460
25.0%
P 1454
24.9%
a 1454
24.9%
e 1454
24.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
v 1460
25.0%
P 1454
24.9%
a 1454
24.9%
e 1454
24.9%
G 6
 
0.1%
r 6
 
0.1%
l 6
 
0.1%

LotShape
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Reg
925 
IR1
484 
IR2
 
41
IR3
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4380
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReg
2nd rowReg
3rd rowIR1
4th rowIR1
5th rowIR1

Common Values

ValueCountFrequency (%)
Reg 925
63.4%
IR1 484
33.2%
IR2 41
 
2.8%
IR3 10
 
0.7%

Length

2023-02-02T13:33:04.013767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:04.103182image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
reg 925
63.4%
ir1 484
33.2%
ir2 41
 
2.8%
ir3 10
 
0.7%

Most occurring characters

ValueCountFrequency (%)
R 1460
33.3%
e 925
21.1%
g 925
21.1%
I 535
 
12.2%
1 484
 
11.1%
2 41
 
0.9%
3 10
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1995
45.5%
Lowercase Letter 1850
42.2%
Decimal Number 535
 
12.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 484
90.5%
2 41
 
7.7%
3 10
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
R 1460
73.2%
I 535
 
26.8%
Lowercase Letter
ValueCountFrequency (%)
e 925
50.0%
g 925
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3845
87.8%
Common 535
 
12.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 1460
38.0%
e 925
24.1%
g 925
24.1%
I 535
 
13.9%
Common
ValueCountFrequency (%)
1 484
90.5%
2 41
 
7.7%
3 10
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 1460
33.3%
e 925
21.1%
g 925
21.1%
I 535
 
12.2%
1 484
 
11.1%
2 41
 
0.9%
3 10
 
0.2%

LandContour
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Lvl
1311 
Bnk
 
63
HLS
 
50
Low
 
36

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4380
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLvl
2nd rowLvl
3rd rowLvl
4th rowLvl
5th rowLvl

Common Values

ValueCountFrequency (%)
Lvl 1311
89.8%
Bnk 63
 
4.3%
HLS 50
 
3.4%
Low 36
 
2.5%

Length

2023-02-02T13:33:04.179021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:04.267903image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
lvl 1311
89.8%
bnk 63
 
4.3%
hls 50
 
3.4%
low 36
 
2.5%

Most occurring characters

ValueCountFrequency (%)
L 1397
31.9%
v 1311
29.9%
l 1311
29.9%
B 63
 
1.4%
n 63
 
1.4%
k 63
 
1.4%
H 50
 
1.1%
S 50
 
1.1%
o 36
 
0.8%
w 36
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2820
64.4%
Uppercase Letter 1560
35.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
v 1311
46.5%
l 1311
46.5%
n 63
 
2.2%
k 63
 
2.2%
o 36
 
1.3%
w 36
 
1.3%
Uppercase Letter
ValueCountFrequency (%)
L 1397
89.6%
B 63
 
4.0%
H 50
 
3.2%
S 50
 
3.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 4380
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 1397
31.9%
v 1311
29.9%
l 1311
29.9%
B 63
 
1.4%
n 63
 
1.4%
k 63
 
1.4%
H 50
 
1.1%
S 50
 
1.1%
o 36
 
0.8%
w 36
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 1397
31.9%
v 1311
29.9%
l 1311
29.9%
B 63
 
1.4%
n 63
 
1.4%
k 63
 
1.4%
H 50
 
1.1%
S 50
 
1.1%
o 36
 
0.8%
w 36
 
0.8%

Utilities
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
AllPub
1459 
NoSeWa
 
1

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters8760
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowAllPub
2nd rowAllPub
3rd rowAllPub
4th rowAllPub
5th rowAllPub

Common Values

ValueCountFrequency (%)
AllPub 1459
99.9%
NoSeWa 1
 
0.1%

Length

2023-02-02T13:33:04.342494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:04.428419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
allpub 1459
99.9%
nosewa 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
l 2918
33.3%
A 1459
16.7%
P 1459
16.7%
u 1459
16.7%
b 1459
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5839
66.7%
Uppercase Letter 2921
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 2918
50.0%
u 1459
25.0%
b 1459
25.0%
o 1
 
< 0.1%
e 1
 
< 0.1%
a 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
A 1459
49.9%
P 1459
49.9%
N 1
 
< 0.1%
S 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 8760
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 2918
33.3%
A 1459
16.7%
P 1459
16.7%
u 1459
16.7%
b 1459
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 2918
33.3%
A 1459
16.7%
P 1459
16.7%
u 1459
16.7%
b 1459
16.7%
N 1
 
< 0.1%
o 1
 
< 0.1%
S 1
 
< 0.1%
e 1
 
< 0.1%
W 1
 
< 0.1%

LotConfig
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Inside
1052 
Corner
263 
CulDSac
 
94
FR2
 
47
FR3
 
4

Length

Max length7
Median length6
Mean length5.959589
Min length3

Characters and Unicode

Total characters8701
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInside
2nd rowFR2
3rd rowInside
4th rowCorner
5th rowFR2

Common Values

ValueCountFrequency (%)
Inside 1052
72.1%
Corner 263
 
18.0%
CulDSac 94
 
6.4%
FR2 47
 
3.2%
FR3 4
 
0.3%

Length

2023-02-02T13:33:04.505395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:04.601911image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
inside 1052
72.1%
corner 263
 
18.0%
culdsac 94
 
6.4%
fr2 47
 
3.2%
fr3 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 1315
15.1%
n 1315
15.1%
I 1052
12.1%
s 1052
12.1%
i 1052
12.1%
d 1052
12.1%
r 526
 
6.0%
C 357
 
4.1%
o 263
 
3.0%
S 94
 
1.1%
Other values (9) 623
7.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6951
79.9%
Uppercase Letter 1699
 
19.5%
Decimal Number 51
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1315
18.9%
n 1315
18.9%
s 1052
15.1%
i 1052
15.1%
d 1052
15.1%
r 526
 
7.6%
o 263
 
3.8%
c 94
 
1.4%
a 94
 
1.4%
u 94
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
I 1052
61.9%
C 357
 
21.0%
S 94
 
5.5%
D 94
 
5.5%
F 51
 
3.0%
R 51
 
3.0%
Decimal Number
ValueCountFrequency (%)
2 47
92.2%
3 4
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 8650
99.4%
Common 51
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1315
15.2%
n 1315
15.2%
I 1052
12.2%
s 1052
12.2%
i 1052
12.2%
d 1052
12.2%
r 526
 
6.1%
C 357
 
4.1%
o 263
 
3.0%
S 94
 
1.1%
Other values (7) 572
6.6%
Common
ValueCountFrequency (%)
2 47
92.2%
3 4
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8701
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1315
15.1%
n 1315
15.1%
I 1052
12.1%
s 1052
12.1%
i 1052
12.1%
d 1052
12.1%
r 526
 
6.0%
C 357
 
4.1%
o 263
 
3.0%
S 94
 
1.1%
Other values (9) 623
7.2%

LandSlope
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Gtl
1382 
Mod
 
65
Sev
 
13

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4380
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGtl
2nd rowGtl
3rd rowGtl
4th rowGtl
5th rowGtl

Common Values

ValueCountFrequency (%)
Gtl 1382
94.7%
Mod 65
 
4.5%
Sev 13
 
0.9%

Length

2023-02-02T13:33:04.682949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:04.770096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
gtl 1382
94.7%
mod 65
 
4.5%
sev 13
 
0.9%

Most occurring characters

ValueCountFrequency (%)
G 1382
31.6%
t 1382
31.6%
l 1382
31.6%
M 65
 
1.5%
o 65
 
1.5%
d 65
 
1.5%
S 13
 
0.3%
e 13
 
0.3%
v 13
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2920
66.7%
Uppercase Letter 1460
33.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1382
47.3%
l 1382
47.3%
o 65
 
2.2%
d 65
 
2.2%
e 13
 
0.4%
v 13
 
0.4%
Uppercase Letter
ValueCountFrequency (%)
G 1382
94.7%
M 65
 
4.5%
S 13
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 4380
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 1382
31.6%
t 1382
31.6%
l 1382
31.6%
M 65
 
1.5%
o 65
 
1.5%
d 65
 
1.5%
S 13
 
0.3%
e 13
 
0.3%
v 13
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4380
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 1382
31.6%
t 1382
31.6%
l 1382
31.6%
M 65
 
1.5%
o 65
 
1.5%
d 65
 
1.5%
S 13
 
0.3%
e 13
 
0.3%
v 13
 
0.3%

Neighborhood
Categorical

Distinct25
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
NAmes
225 
CollgCr
150 
OldTown
113 
Edwards
100 
Somerst
86 
Other values (20)
786 

Length

Max length7
Median length7
Mean length6.4945205
Min length5

Characters and Unicode

Total characters9482
Distinct characters38
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCollgCr
2nd rowVeenker
3rd rowCollgCr
4th rowCrawfor
5th rowNoRidge

Common Values

ValueCountFrequency (%)
NAmes 225
15.4%
CollgCr 150
 
10.3%
OldTown 113
 
7.7%
Edwards 100
 
6.8%
Somerst 86
 
5.9%
Gilbert 79
 
5.4%
NridgHt 77
 
5.3%
Sawyer 74
 
5.1%
NWAmes 73
 
5.0%
SawyerW 59
 
4.0%
Other values (15) 424
29.0%

Length

2023-02-02T13:33:04.846952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
names 225
15.4%
collgcr 150
 
10.3%
oldtown 113
 
7.7%
edwards 100
 
6.8%
somerst 86
 
5.9%
gilbert 79
 
5.4%
nridght 77
 
5.3%
sawyer 74
 
5.1%
nwames 73
 
5.0%
sawyerw 59
 
4.0%
Other values (15) 424
29.0%

Most occurring characters

ValueCountFrequency (%)
r 931
 
9.8%
e 905
 
9.5%
l 622
 
6.6%
d 506
 
5.3%
s 486
 
5.1%
o 483
 
5.1%
m 439
 
4.6%
N 425
 
4.5%
w 414
 
4.4%
C 407
 
4.3%
Other values (28) 3864
40.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6764
71.3%
Uppercase Letter 2718
28.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 931
13.8%
e 905
13.4%
l 622
9.2%
d 506
 
7.5%
s 486
 
7.2%
o 483
 
7.1%
m 439
 
6.5%
w 414
 
6.1%
i 351
 
5.2%
a 345
 
5.1%
Other values (10) 1282
19.0%
Uppercase Letter
ValueCountFrequency (%)
N 425
15.6%
C 407
15.0%
S 352
13.0%
A 298
11.0%
T 188
6.9%
W 157
 
5.8%
O 150
 
5.5%
B 118
 
4.3%
R 115
 
4.2%
E 100
 
3.7%
Other values (8) 408
15.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9482
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 931
 
9.8%
e 905
 
9.5%
l 622
 
6.6%
d 506
 
5.3%
s 486
 
5.1%
o 483
 
5.1%
m 439
 
4.6%
N 425
 
4.5%
w 414
 
4.4%
C 407
 
4.3%
Other values (28) 3864
40.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9482
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 931
 
9.8%
e 905
 
9.5%
l 622
 
6.6%
d 506
 
5.3%
s 486
 
5.1%
o 483
 
5.1%
m 439
 
4.6%
N 425
 
4.5%
w 414
 
4.4%
C 407
 
4.3%
Other values (28) 3864
40.8%

Condition1
Categorical

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Norm
1260 
Feedr
 
81
Artery
 
48
RRAn
 
26
PosN
 
19
Other values (4)
 
26

Length

Max length6
Median length4
Mean length4.1212329
Min length4

Characters and Unicode

Total characters6017
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorm
2nd rowFeedr
3rd rowNorm
4th rowNorm
5th rowNorm

Common Values

ValueCountFrequency (%)
Norm 1260
86.3%
Feedr 81
 
5.5%
Artery 48
 
3.3%
RRAn 26
 
1.8%
PosN 19
 
1.3%
RRAe 11
 
0.8%
PosA 8
 
0.5%
RRNn 5
 
0.3%
RRNe 2
 
0.1%

Length

2023-02-02T13:33:04.940067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:05.042982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
norm 1260
86.3%
feedr 81
 
5.5%
artery 48
 
3.3%
rran 26
 
1.8%
posn 19
 
1.3%
rrae 11
 
0.8%
posa 8
 
0.5%
rrnn 5
 
0.3%
rrne 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 1437
23.9%
o 1287
21.4%
N 1286
21.4%
m 1260
20.9%
e 223
 
3.7%
A 93
 
1.5%
R 88
 
1.5%
F 81
 
1.3%
d 81
 
1.3%
t 48
 
0.8%
Other values (4) 133
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4442
73.8%
Uppercase Letter 1575
 
26.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1437
32.4%
o 1287
29.0%
m 1260
28.4%
e 223
 
5.0%
d 81
 
1.8%
t 48
 
1.1%
y 48
 
1.1%
n 31
 
0.7%
s 27
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
N 1286
81.7%
A 93
 
5.9%
R 88
 
5.6%
F 81
 
5.1%
P 27
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 6017
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1437
23.9%
o 1287
21.4%
N 1286
21.4%
m 1260
20.9%
e 223
 
3.7%
A 93
 
1.5%
R 88
 
1.5%
F 81
 
1.3%
d 81
 
1.3%
t 48
 
0.8%
Other values (4) 133
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6017
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1437
23.9%
o 1287
21.4%
N 1286
21.4%
m 1260
20.9%
e 223
 
3.7%
A 93
 
1.5%
R 88
 
1.5%
F 81
 
1.3%
d 81
 
1.3%
t 48
 
0.8%
Other values (4) 133
 
2.2%

Condition2
Categorical

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Norm
1445 
Feedr
 
6
Artery
 
2
RRNn
 
2
PosN
 
2
Other values (3)
 
3

Length

Max length6
Median length4
Mean length4.0068493
Min length4

Characters and Unicode

Total characters5850
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowNorm
2nd rowNorm
3rd rowNorm
4th rowNorm
5th rowNorm

Common Values

ValueCountFrequency (%)
Norm 1445
99.0%
Feedr 6
 
0.4%
Artery 2
 
0.1%
RRNn 2
 
0.1%
PosN 2
 
0.1%
PosA 1
 
0.1%
RRAn 1
 
0.1%
RRAe 1
 
0.1%

Length

2023-02-02T13:33:05.132629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:05.236582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
norm 1445
99.0%
feedr 6
 
0.4%
artery 2
 
0.1%
rrnn 2
 
0.1%
posn 2
 
0.1%
posa 1
 
0.1%
rran 1
 
0.1%
rrae 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
r 1455
24.9%
N 1449
24.8%
o 1448
24.8%
m 1445
24.7%
e 15
 
0.3%
R 8
 
0.1%
F 6
 
0.1%
d 6
 
0.1%
A 5
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4379
74.9%
Uppercase Letter 1471
 
25.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 1455
33.2%
o 1448
33.1%
m 1445
33.0%
e 15
 
0.3%
d 6
 
0.1%
n 3
 
0.1%
s 3
 
0.1%
t 2
 
< 0.1%
y 2
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
N 1449
98.5%
R 8
 
0.5%
F 6
 
0.4%
A 5
 
0.3%
P 3
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 5850
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 1455
24.9%
N 1449
24.8%
o 1448
24.8%
m 1445
24.7%
e 15
 
0.3%
R 8
 
0.1%
F 6
 
0.1%
d 6
 
0.1%
A 5
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5850
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 1455
24.9%
N 1449
24.8%
o 1448
24.8%
m 1445
24.7%
e 15
 
0.3%
R 8
 
0.1%
F 6
 
0.1%
d 6
 
0.1%
A 5
 
0.1%
n 3
 
0.1%
Other values (4) 10
 
0.2%

BldgType
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1Fam
1220 
TwnhsE
 
114
Duplex
 
52
Twnhs
 
43
2fmCon
 
31

Length

Max length6
Median length4
Mean length4.2993151
Min length4

Characters and Unicode

Total characters6277
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1Fam
2nd row1Fam
3rd row1Fam
4th row1Fam
5th row1Fam

Common Values

ValueCountFrequency (%)
1Fam 1220
83.6%
TwnhsE 114
 
7.8%
Duplex 52
 
3.6%
Twnhs 43
 
2.9%
2fmCon 31
 
2.1%

Length

2023-02-02T13:33:05.336907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:05.434907image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1fam 1220
83.6%
twnhse 114
 
7.8%
duplex 52
 
3.6%
twnhs 43
 
2.9%
2fmcon 31
 
2.1%

Most occurring characters

ValueCountFrequency (%)
m 1251
19.9%
1 1220
19.4%
a 1220
19.4%
F 1220
19.4%
n 188
 
3.0%
T 157
 
2.5%
w 157
 
2.5%
h 157
 
2.5%
s 157
 
2.5%
E 114
 
1.8%
Other values (10) 436
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3452
55.0%
Uppercase Letter 1574
25.1%
Decimal Number 1251
 
19.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 1251
36.2%
a 1220
35.3%
n 188
 
5.4%
w 157
 
4.5%
h 157
 
4.5%
s 157
 
4.5%
l 52
 
1.5%
x 52
 
1.5%
e 52
 
1.5%
p 52
 
1.5%
Other values (3) 114
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
F 1220
77.5%
T 157
 
10.0%
E 114
 
7.2%
D 52
 
3.3%
C 31
 
2.0%
Decimal Number
ValueCountFrequency (%)
1 1220
97.5%
2 31
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 5026
80.1%
Common 1251
 
19.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 1251
24.9%
a 1220
24.3%
F 1220
24.3%
n 188
 
3.7%
T 157
 
3.1%
w 157
 
3.1%
h 157
 
3.1%
s 157
 
3.1%
E 114
 
2.3%
l 52
 
1.0%
Other values (8) 353
 
7.0%
Common
ValueCountFrequency (%)
1 1220
97.5%
2 31
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6277
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 1251
19.9%
1 1220
19.4%
a 1220
19.4%
F 1220
19.4%
n 188
 
3.0%
T 157
 
2.5%
w 157
 
2.5%
h 157
 
2.5%
s 157
 
2.5%
E 114
 
1.8%
Other values (10) 436
 
6.9%

HouseStyle
Categorical

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1Story
726 
2Story
445 
1.5Fin
154 
SLvl
 
65
SFoyer
 
37
Other values (3)
 
33

Length

Max length6
Median length6
Mean length5.9109589
Min length4

Characters and Unicode

Total characters8630
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2Story
2nd row1Story
3rd row2Story
4th row2Story
5th row2Story

Common Values

ValueCountFrequency (%)
1Story 726
49.7%
2Story 445
30.5%
1.5Fin 154
 
10.5%
SLvl 65
 
4.5%
SFoyer 37
 
2.5%
1.5Unf 14
 
1.0%
2.5Unf 11
 
0.8%
2.5Fin 8
 
0.5%

Length

2023-02-02T13:33:05.517693image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:05.619246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1story 726
49.7%
2story 445
30.5%
1.5fin 154
 
10.5%
slvl 65
 
4.5%
sfoyer 37
 
2.5%
1.5unf 14
 
1.0%
2.5unf 11
 
0.8%
2.5fin 8
 
0.5%

Most occurring characters

ValueCountFrequency (%)
S 1273
14.8%
o 1208
14.0%
r 1208
14.0%
y 1208
14.0%
t 1171
13.6%
1 894
10.4%
2 464
 
5.4%
F 199
 
2.3%
5 187
 
2.2%
. 187
 
2.2%
Other values (8) 631
7.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5336
61.8%
Uppercase Letter 1562
 
18.1%
Decimal Number 1545
 
17.9%
Other Punctuation 187
 
2.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1208
22.6%
r 1208
22.6%
y 1208
22.6%
t 1171
21.9%
n 187
 
3.5%
i 162
 
3.0%
v 65
 
1.2%
l 65
 
1.2%
e 37
 
0.7%
f 25
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
S 1273
81.5%
F 199
 
12.7%
L 65
 
4.2%
U 25
 
1.6%
Decimal Number
ValueCountFrequency (%)
1 894
57.9%
2 464
30.0%
5 187
 
12.1%
Other Punctuation
ValueCountFrequency (%)
. 187
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6898
79.9%
Common 1732
 
20.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 1273
18.5%
o 1208
17.5%
r 1208
17.5%
y 1208
17.5%
t 1171
17.0%
F 199
 
2.9%
n 187
 
2.7%
i 162
 
2.3%
L 65
 
0.9%
v 65
 
0.9%
Other values (4) 152
 
2.2%
Common
ValueCountFrequency (%)
1 894
51.6%
2 464
26.8%
5 187
 
10.8%
. 187
 
10.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8630
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 1273
14.8%
o 1208
14.0%
r 1208
14.0%
y 1208
14.0%
t 1171
13.6%
1 894
10.4%
2 464
 
5.4%
F 199
 
2.3%
5 187
 
2.2%
. 187
 
2.2%
Other values (8) 631
7.3%

OverallQual
Real number (ℝ)

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.0993151
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:05.699776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q37
95-th percentile8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3829965
Coefficient of variation (CV)0.22674621
Kurtosis0.096292778
Mean6.0993151
Median Absolute Deviation (MAD)1
Skewness0.21694393
Sum8905
Variance1.9126794
MonotonicityNot monotonic
2023-02-02T13:33:05.772881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
5 397
27.2%
6 374
25.6%
7 319
21.8%
8 168
11.5%
4 116
 
7.9%
9 43
 
2.9%
3 20
 
1.4%
10 18
 
1.2%
2 3
 
0.2%
1 2
 
0.1%
ValueCountFrequency (%)
1 2
 
0.1%
2 3
 
0.2%
3 20
 
1.4%
4 116
 
7.9%
5 397
27.2%
6 374
25.6%
7 319
21.8%
8 168
11.5%
9 43
 
2.9%
10 18
 
1.2%
ValueCountFrequency (%)
10 18
 
1.2%
9 43
 
2.9%
8 168
11.5%
7 319
21.8%
6 374
25.6%
5 397
27.2%
4 116
 
7.9%
3 20
 
1.4%
2 3
 
0.2%
1 2
 
0.1%

OverallCond
Real number (ℝ)

Distinct9
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5753425
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:05.848472image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median5
Q36
95-th percentile8
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1127993
Coefficient of variation (CV)0.199593
Kurtosis1.1064135
Mean5.5753425
Median Absolute Deviation (MAD)0
Skewness0.69306747
Sum8140
Variance1.2383224
MonotonicityNot monotonic
2023-02-02T13:33:05.924120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 821
56.2%
6 252
 
17.3%
7 205
 
14.0%
8 72
 
4.9%
4 57
 
3.9%
3 25
 
1.7%
9 22
 
1.5%
2 5
 
0.3%
1 1
 
0.1%
ValueCountFrequency (%)
1 1
 
0.1%
2 5
 
0.3%
3 25
 
1.7%
4 57
 
3.9%
5 821
56.2%
6 252
 
17.3%
7 205
 
14.0%
8 72
 
4.9%
9 22
 
1.5%
ValueCountFrequency (%)
9 22
 
1.5%
8 72
 
4.9%
7 205
 
14.0%
6 252
 
17.3%
5 821
56.2%
4 57
 
3.9%
3 25
 
1.7%
2 5
 
0.3%
1 1
 
0.1%

RoofStyle
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Gable
1141 
Hip
286 
Flat
 
13
Gambrel
 
11
Mansard
 
7

Length

Max length7
Median length5
Mean length4.6226027
Min length3

Characters and Unicode

Total characters6749
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGable
2nd rowGable
3rd rowGable
4th rowGable
5th rowGable

Common Values

ValueCountFrequency (%)
Gable 1141
78.2%
Hip 286
 
19.6%
Flat 13
 
0.9%
Gambrel 11
 
0.8%
Mansard 7
 
0.5%
Shed 2
 
0.1%

Length

2023-02-02T13:33:06.015505image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:06.112349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
gable 1141
78.2%
hip 286
 
19.6%
flat 13
 
0.9%
gambrel 11
 
0.8%
mansard 7
 
0.5%
shed 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 1179
17.5%
l 1165
17.3%
e 1154
17.1%
G 1152
17.1%
b 1152
17.1%
H 286
 
4.2%
i 286
 
4.2%
p 286
 
4.2%
r 18
 
0.3%
t 13
 
0.2%
Other values (8) 58
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5289
78.4%
Uppercase Letter 1460
 
21.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1179
22.3%
l 1165
22.0%
e 1154
21.8%
b 1152
21.8%
i 286
 
5.4%
p 286
 
5.4%
r 18
 
0.3%
t 13
 
0.2%
m 11
 
0.2%
d 9
 
0.2%
Other values (3) 16
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
G 1152
78.9%
H 286
 
19.6%
F 13
 
0.9%
M 7
 
0.5%
S 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 6749
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1179
17.5%
l 1165
17.3%
e 1154
17.1%
G 1152
17.1%
b 1152
17.1%
H 286
 
4.2%
i 286
 
4.2%
p 286
 
4.2%
r 18
 
0.3%
t 13
 
0.2%
Other values (8) 58
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6749
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1179
17.5%
l 1165
17.3%
e 1154
17.1%
G 1152
17.1%
b 1152
17.1%
H 286
 
4.2%
i 286
 
4.2%
p 286
 
4.2%
r 18
 
0.3%
t 13
 
0.2%
Other values (8) 58
 
0.9%

RoofMatl
Categorical

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
CompShg
1434 
Tar&Grv
 
11
WdShngl
 
6
WdShake
 
5
Metal
 
1
Other values (3)
 
3

Length

Max length7
Median length7
Mean length6.9965753
Min length4

Characters and Unicode

Total characters10215
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.3%

Sample

1st rowCompShg
2nd rowCompShg
3rd rowCompShg
4th rowCompShg
5th rowCompShg

Common Values

ValueCountFrequency (%)
CompShg 1434
98.2%
Tar&Grv 11
 
0.8%
WdShngl 6
 
0.4%
WdShake 5
 
0.3%
Metal 1
 
0.1%
Membran 1
 
0.1%
Roll 1
 
0.1%
ClyTile 1
 
0.1%

Length

2023-02-02T13:33:06.196836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:06.295149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
compshg 1434
98.2%
tar&grv 11
 
0.8%
wdshngl 6
 
0.4%
wdshake 5
 
0.3%
metal 1
 
0.1%
membran 1
 
0.1%
roll 1
 
0.1%
clytile 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
S 1445
14.1%
h 1445
14.1%
g 1440
14.1%
C 1435
14.0%
m 1435
14.0%
o 1435
14.0%
p 1434
14.0%
r 23
 
0.2%
a 18
 
0.2%
T 12
 
0.1%
Other values (15) 93
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7287
71.3%
Uppercase Letter 2917
28.6%
Other Punctuation 11
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h 1445
19.8%
g 1440
19.8%
m 1435
19.7%
o 1435
19.7%
p 1434
19.7%
r 23
 
0.3%
a 18
 
0.2%
l 11
 
0.2%
d 11
 
0.2%
v 11
 
0.2%
Other values (7) 24
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
S 1445
49.5%
C 1435
49.2%
T 12
 
0.4%
W 11
 
0.4%
G 11
 
0.4%
M 2
 
0.1%
R 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
& 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10204
99.9%
Common 11
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 1445
14.2%
h 1445
14.2%
g 1440
14.1%
C 1435
14.1%
m 1435
14.1%
o 1435
14.1%
p 1434
14.1%
r 23
 
0.2%
a 18
 
0.2%
T 12
 
0.1%
Other values (14) 82
 
0.8%
Common
ValueCountFrequency (%)
& 11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10215
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 1445
14.1%
h 1445
14.1%
g 1440
14.1%
C 1435
14.0%
m 1435
14.0%
o 1435
14.0%
p 1434
14.0%
r 23
 
0.2%
a 18
 
0.2%
T 12
 
0.1%
Other values (15) 93
 
0.9%

Exterior1st
Categorical

Distinct15
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
VinylSd
515 
HdBoard
222 
MetalSd
220 
Wd Sdng
206 
Plywood
108 
Other values (10)
189 

Length

Max length7
Median length7
Mean length6.9794521
Min length5

Characters and Unicode

Total characters10190
Distinct characters32
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Sdng
5th rowVinylSd

Common Values

ValueCountFrequency (%)
VinylSd 515
35.3%
HdBoard 222
15.2%
MetalSd 220
15.1%
Wd Sdng 206
 
14.1%
Plywood 108
 
7.4%
CemntBd 61
 
4.2%
BrkFace 50
 
3.4%
WdShing 26
 
1.8%
Stucco 25
 
1.7%
AsbShng 20
 
1.4%
Other values (5) 7
 
0.5%

Length

2023-02-02T13:33:06.384820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vinylsd 515
30.9%
hdboard 222
13.3%
metalsd 220
13.2%
wd 206
 
12.4%
sdng 206
 
12.4%
plywood 108
 
6.5%
cemntbd 61
 
3.7%
brkface 50
 
3.0%
wdshing 26
 
1.6%
stucco 25
 
1.5%
Other values (6) 27
 
1.6%

Most occurring characters

ValueCountFrequency (%)
d 1786
17.5%
S 1016
 
10.0%
l 844
 
8.3%
n 831
 
8.2%
y 623
 
6.1%
i 541
 
5.3%
V 515
 
5.1%
a 492
 
4.8%
o 468
 
4.6%
B 336
 
3.3%
Other values (22) 2738
26.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7199
70.6%
Uppercase Letter 2785
 
27.3%
Space Separator 206
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 1786
24.8%
l 844
11.7%
n 831
11.5%
y 623
 
8.7%
i 541
 
7.5%
a 492
 
6.8%
o 468
 
6.5%
e 333
 
4.6%
t 309
 
4.3%
r 274
 
3.8%
Other values (10) 698
 
9.7%
Uppercase Letter
ValueCountFrequency (%)
S 1016
36.5%
V 515
18.5%
B 336
 
12.1%
W 232
 
8.3%
H 222
 
8.0%
M 220
 
7.9%
P 108
 
3.9%
C 64
 
2.3%
F 50
 
1.8%
A 21
 
0.8%
Space Separator
ValueCountFrequency (%)
206
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9984
98.0%
Common 206
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 1786
17.9%
S 1016
10.2%
l 844
 
8.5%
n 831
 
8.3%
y 623
 
6.2%
i 541
 
5.4%
V 515
 
5.2%
a 492
 
4.9%
o 468
 
4.7%
B 336
 
3.4%
Other values (21) 2532
25.4%
Common
ValueCountFrequency (%)
206
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10190
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 1786
17.5%
S 1016
 
10.0%
l 844
 
8.3%
n 831
 
8.2%
y 623
 
6.1%
i 541
 
5.3%
V 515
 
5.1%
a 492
 
4.8%
o 468
 
4.6%
B 336
 
3.3%
Other values (22) 2738
26.9%

Exterior2nd
Categorical

Distinct16
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
VinylSd
504 
MetalSd
214 
HdBoard
207 
Wd Sdng
197 
Plywood
142 
Other values (11)
196 

Length

Max length7
Median length7
Mean length6.9732877
Min length5

Characters and Unicode

Total characters10181
Distinct characters33
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowVinylSd
2nd rowMetalSd
3rd rowVinylSd
4th rowWd Shng
5th rowVinylSd

Common Values

ValueCountFrequency (%)
VinylSd 504
34.5%
MetalSd 214
14.7%
HdBoard 207
14.2%
Wd Sdng 197
 
13.5%
Plywood 142
 
9.7%
CmentBd 60
 
4.1%
Wd Shng 38
 
2.6%
Stucco 26
 
1.8%
BrkFace 25
 
1.7%
AsbShng 20
 
1.4%
Other values (6) 27
 
1.8%

Length

2023-02-02T13:33:06.497418image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
vinylsd 504
29.6%
wd 235
13.8%
metalsd 214
12.6%
hdboard 207
12.2%
sdng 197
 
11.6%
plywood 142
 
8.3%
cmentbd 60
 
3.5%
shng 38
 
2.2%
stucco 26
 
1.5%
brkface 25
 
1.5%
Other values (8) 54
 
3.2%

Most occurring characters

ValueCountFrequency (%)
d 1766
17.3%
S 1017
 
10.0%
l 861
 
8.5%
n 834
 
8.2%
y 646
 
6.3%
o 523
 
5.1%
V 504
 
5.0%
i 504
 
5.0%
a 446
 
4.4%
t 316
 
3.1%
Other values (23) 2764
27.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7193
70.7%
Uppercase Letter 2746
 
27.0%
Space Separator 242
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 1766
24.6%
l 861
12.0%
n 834
11.6%
y 646
 
9.0%
o 523
 
7.3%
i 504
 
7.0%
a 446
 
6.2%
t 316
 
4.4%
e 305
 
4.2%
g 255
 
3.5%
Other values (10) 737
10.2%
Uppercase Letter
ValueCountFrequency (%)
S 1017
37.0%
V 504
18.4%
B 300
 
10.9%
W 235
 
8.6%
M 214
 
7.8%
H 207
 
7.5%
P 142
 
5.2%
C 68
 
2.5%
F 25
 
0.9%
A 23
 
0.8%
Other values (2) 11
 
0.4%
Space Separator
ValueCountFrequency (%)
242
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9939
97.6%
Common 242
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 1766
17.8%
S 1017
10.2%
l 861
 
8.7%
n 834
 
8.4%
y 646
 
6.5%
o 523
 
5.3%
V 504
 
5.1%
i 504
 
5.1%
a 446
 
4.5%
t 316
 
3.2%
Other values (22) 2522
25.4%
Common
ValueCountFrequency (%)
242
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10181
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 1766
17.3%
S 1017
 
10.0%
l 861
 
8.5%
n 834
 
8.2%
y 646
 
6.3%
o 523
 
5.1%
V 504
 
5.0%
i 504
 
5.0%
a 446
 
4.4%
t 316
 
3.1%
Other values (23) 2764
27.1%

MasVnrArea
Real number (ℝ)

Distinct327
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean103.11712
Minimum0
Maximum1600
Zeros869
Zeros (%)59.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:06.602186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3164.25
95-th percentile456
Maximum1600
Range1600
Interquartile range (IQR)164.25

Descriptive statistics

Standard deviation180.73137
Coefficient of variation (CV)1.7526805
Kurtosis10.141416
Mean103.11712
Median Absolute Deviation (MAD)0
Skewness2.6776165
Sum150551
Variance32663.829
MonotonicityNot monotonic
2023-02-02T13:33:06.716961image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 869
59.5%
180 8
 
0.5%
72 8
 
0.5%
108 8
 
0.5%
120 7
 
0.5%
16 7
 
0.5%
200 6
 
0.4%
340 6
 
0.4%
106 6
 
0.4%
80 6
 
0.4%
Other values (317) 529
36.2%
ValueCountFrequency (%)
0 869
59.5%
1 2
 
0.1%
11 1
 
0.1%
14 1
 
0.1%
16 7
 
0.5%
18 2
 
0.1%
22 1
 
0.1%
24 1
 
0.1%
27 1
 
0.1%
28 1
 
0.1%
ValueCountFrequency (%)
1600 1
0.1%
1378 1
0.1%
1170 1
0.1%
1129 1
0.1%
1115 1
0.1%
1047 1
0.1%
1031 1
0.1%
975 1
0.1%
922 1
0.1%
921 1
0.1%

ExterQual
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
TA
906 
Gd
488 
Ex
 
52
Fa
 
14

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowTA
5th rowGd

Common Values

ValueCountFrequency (%)
TA 906
62.1%
Gd 488
33.4%
Ex 52
 
3.6%
Fa 14
 
1.0%

Length

2023-02-02T13:33:06.821362image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:06.912435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
ta 906
62.1%
gd 488
33.4%
ex 52
 
3.6%
fa 14
 
1.0%

Most occurring characters

ValueCountFrequency (%)
T 906
31.0%
A 906
31.0%
G 488
16.7%
d 488
16.7%
E 52
 
1.8%
x 52
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2366
81.0%
Lowercase Letter 554
 
19.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 906
38.3%
A 906
38.3%
G 488
20.6%
E 52
 
2.2%
F 14
 
0.6%
Lowercase Letter
ValueCountFrequency (%)
d 488
88.1%
x 52
 
9.4%
a 14
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 2920
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 906
31.0%
A 906
31.0%
G 488
16.7%
d 488
16.7%
E 52
 
1.8%
x 52
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 906
31.0%
A 906
31.0%
G 488
16.7%
d 488
16.7%
E 52
 
1.8%
x 52
 
1.8%
F 14
 
0.5%
a 14
 
0.5%

ExterCond
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
TA
1282 
Gd
146 
Fa
 
28
Ex
 
3
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTA
2nd rowTA
3rd rowTA
4th rowTA
5th rowTA

Common Values

ValueCountFrequency (%)
TA 1282
87.8%
Gd 146
 
10.0%
Fa 28
 
1.9%
Ex 3
 
0.2%
Po 1
 
0.1%

Length

2023-02-02T13:33:06.989170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:07.079378image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
ta 1282
87.8%
gd 146
 
10.0%
fa 28
 
1.9%
ex 3
 
0.2%
po 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1282
43.9%
A 1282
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2742
93.9%
Lowercase Letter 178
 
6.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1282
46.8%
A 1282
46.8%
G 146
 
5.3%
F 28
 
1.0%
E 3
 
0.1%
P 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
d 146
82.0%
a 28
 
15.7%
x 3
 
1.7%
o 1
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 2920
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1282
43.9%
A 1282
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1282
43.9%
A 1282
43.9%
G 146
 
5.0%
d 146
 
5.0%
F 28
 
1.0%
a 28
 
1.0%
E 3
 
0.1%
x 3
 
0.1%
P 1
 
< 0.1%
o 1
 
< 0.1%

Foundation
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
PConc
647 
CBlock
634 
BrkTil
146 
Slab
 
24
Stone
 
6

Length

Max length6
Median length6
Mean length5.5157534
Min length4

Characters and Unicode

Total characters8053
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPConc
2nd rowCBlock
3rd rowPConc
4th rowBrkTil
5th rowPConc

Common Values

ValueCountFrequency (%)
PConc 647
44.3%
CBlock 634
43.4%
BrkTil 146
 
10.0%
Slab 24
 
1.6%
Stone 6
 
0.4%
Wood 3
 
0.2%

Length

2023-02-02T13:33:07.159627image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:07.257682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
pconc 647
44.3%
cblock 634
43.4%
brktil 146
 
10.0%
slab 24
 
1.6%
stone 6
 
0.4%
wood 3
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o 1293
16.1%
C 1281
15.9%
c 1281
15.9%
l 804
10.0%
B 780
9.7%
k 780
9.7%
n 653
8.1%
P 647
8.0%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 242
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5166
64.2%
Uppercase Letter 2887
35.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 1293
25.0%
c 1281
24.8%
l 804
15.6%
k 780
15.1%
n 653
12.6%
i 146
 
2.8%
r 146
 
2.8%
a 24
 
0.5%
b 24
 
0.5%
t 6
 
0.1%
Other values (2) 9
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
C 1281
44.4%
B 780
27.0%
P 647
22.4%
T 146
 
5.1%
S 30
 
1.0%
W 3
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 8053
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 1293
16.1%
C 1281
15.9%
c 1281
15.9%
l 804
10.0%
B 780
9.7%
k 780
9.7%
n 653
8.1%
P 647
8.0%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 242
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8053
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 1293
16.1%
C 1281
15.9%
c 1281
15.9%
l 804
10.0%
B 780
9.7%
k 780
9.7%
n 653
8.1%
P 647
8.0%
i 146
 
1.8%
T 146
 
1.8%
Other values (8) 242
 
3.0%

BsmtFinSF1
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct637
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean443.63973
Minimum0
Maximum5644
Zeros467
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:07.346465image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median383.5
Q3712.25
95-th percentile1274
Maximum5644
Range5644
Interquartile range (IQR)712.25

Descriptive statistics

Standard deviation456.09809
Coefficient of variation (CV)1.0280822
Kurtosis11.118236
Mean443.63973
Median Absolute Deviation (MAD)383.5
Skewness1.6855031
Sum647714
Variance208025.47
MonotonicityNot monotonic
2023-02-02T13:33:07.440845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 467
32.0%
24 12
 
0.8%
16 9
 
0.6%
686 5
 
0.3%
662 5
 
0.3%
20 5
 
0.3%
936 5
 
0.3%
616 5
 
0.3%
560 4
 
0.3%
553 4
 
0.3%
Other values (627) 939
64.3%
ValueCountFrequency (%)
0 467
32.0%
2 1
 
0.1%
16 9
 
0.6%
20 5
 
0.3%
24 12
 
0.8%
25 1
 
0.1%
27 1
 
0.1%
28 3
 
0.2%
33 1
 
0.1%
35 1
 
0.1%
ValueCountFrequency (%)
5644 1
0.1%
2260 1
0.1%
2188 1
0.1%
2096 1
0.1%
1904 1
0.1%
1880 1
0.1%
1810 1
0.1%
1767 1
0.1%
1721 1
0.1%
1696 1
0.1%

BsmtFinSF2
Real number (ℝ)

Distinct144
Distinct (%)9.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.549315
Minimum0
Maximum1474
Zeros1293
Zeros (%)88.6%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:07.541156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile396.2
Maximum1474
Range1474
Interquartile range (IQR)0

Descriptive statistics

Standard deviation161.31927
Coefficient of variation (CV)3.4655563
Kurtosis20.113338
Mean46.549315
Median Absolute Deviation (MAD)0
Skewness4.2552611
Sum67962
Variance26023.908
MonotonicityNot monotonic
2023-02-02T13:33:07.638861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1293
88.6%
180 5
 
0.3%
374 3
 
0.2%
551 2
 
0.1%
147 2
 
0.1%
294 2
 
0.1%
391 2
 
0.1%
539 2
 
0.1%
96 2
 
0.1%
480 2
 
0.1%
Other values (134) 145
 
9.9%
ValueCountFrequency (%)
0 1293
88.6%
28 1
 
0.1%
32 1
 
0.1%
35 1
 
0.1%
40 1
 
0.1%
41 2
 
0.1%
64 2
 
0.1%
68 1
 
0.1%
80 1
 
0.1%
81 1
 
0.1%
ValueCountFrequency (%)
1474 1
0.1%
1127 1
0.1%
1120 1
0.1%
1085 1
0.1%
1080 1
0.1%
1063 1
0.1%
1061 1
0.1%
1057 1
0.1%
1031 1
0.1%
1029 1
0.1%

BsmtUnfSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct780
Distinct (%)53.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.24041
Minimum0
Maximum2336
Zeros118
Zeros (%)8.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:07.740028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1223
median477.5
Q3808
95-th percentile1468
Maximum2336
Range2336
Interquartile range (IQR)585

Descriptive statistics

Standard deviation441.86696
Coefficient of variation (CV)0.77897651
Kurtosis0.47499399
Mean567.24041
Median Absolute Deviation (MAD)288
Skewness0.92026845
Sum828171
Variance195246.41
MonotonicityNot monotonic
2023-02-02T13:33:07.838211image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118
 
8.1%
728 9
 
0.6%
384 8
 
0.5%
600 7
 
0.5%
300 7
 
0.5%
572 7
 
0.5%
270 6
 
0.4%
625 6
 
0.4%
672 6
 
0.4%
440 6
 
0.4%
Other values (770) 1280
87.7%
ValueCountFrequency (%)
0 118
8.1%
14 1
 
0.1%
15 1
 
0.1%
23 2
 
0.1%
26 1
 
0.1%
29 1
 
0.1%
30 1
 
0.1%
32 2
 
0.1%
35 1
 
0.1%
36 4
 
0.3%
ValueCountFrequency (%)
2336 1
0.1%
2153 1
0.1%
2121 1
0.1%
2046 1
0.1%
2042 1
0.1%
2002 1
0.1%
1969 1
0.1%
1935 1
0.1%
1926 1
0.1%
1907 1
0.1%

TotalBsmtSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct721
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1057.4295
Minimum0
Maximum6110
Zeros37
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:07.936415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile519.3
Q1795.75
median991.5
Q31298.25
95-th percentile1753
Maximum6110
Range6110
Interquartile range (IQR)502.5

Descriptive statistics

Standard deviation438.70532
Coefficient of variation (CV)0.41487905
Kurtosis13.250483
Mean1057.4295
Median Absolute Deviation (MAD)234.5
Skewness1.5242545
Sum1543847
Variance192462.36
MonotonicityNot monotonic
2023-02-02T13:33:08.032544image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
2.5%
864 35
 
2.4%
672 17
 
1.2%
912 15
 
1.0%
1040 14
 
1.0%
816 13
 
0.9%
768 12
 
0.8%
728 12
 
0.8%
894 11
 
0.8%
780 11
 
0.8%
Other values (711) 1283
87.9%
ValueCountFrequency (%)
0 37
2.5%
105 1
 
0.1%
190 1
 
0.1%
264 3
 
0.2%
270 1
 
0.1%
290 1
 
0.1%
319 1
 
0.1%
360 1
 
0.1%
372 1
 
0.1%
384 7
 
0.5%
ValueCountFrequency (%)
6110 1
0.1%
3206 1
0.1%
3200 1
0.1%
3138 1
0.1%
3094 1
0.1%
2633 1
0.1%
2524 1
0.1%
2444 1
0.1%
2396 1
0.1%
2392 1
0.1%

Heating
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
GasA
1428 
GasW
 
18
Grav
 
7
Wall
 
4
OthW
 
2

Length

Max length5
Median length4
Mean length4.0006849
Min length4

Characters and Unicode

Total characters5841
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowGasA
2nd rowGasA
3rd rowGasA
4th rowGasA
5th rowGasA

Common Values

ValueCountFrequency (%)
GasA 1428
97.8%
GasW 18
 
1.2%
Grav 7
 
0.5%
Wall 4
 
0.3%
OthW 2
 
0.1%
Floor 1
 
0.1%

Length

2023-02-02T13:33:08.127990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:08.221025image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
gasa 1428
97.8%
gasw 18
 
1.2%
grav 7
 
0.5%
wall 4
 
0.3%
othw 2
 
0.1%
floor 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
a 1457
24.9%
G 1453
24.9%
s 1446
24.8%
A 1428
24.4%
W 24
 
0.4%
l 9
 
0.2%
r 8
 
0.1%
v 7
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2933
50.2%
Uppercase Letter 2908
49.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1457
49.7%
s 1446
49.3%
l 9
 
0.3%
r 8
 
0.3%
v 7
 
0.2%
t 2
 
0.1%
h 2
 
0.1%
o 2
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
G 1453
50.0%
A 1428
49.1%
W 24
 
0.8%
O 2
 
0.1%
F 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 5841
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1457
24.9%
G 1453
24.9%
s 1446
24.8%
A 1428
24.4%
W 24
 
0.4%
l 9
 
0.2%
r 8
 
0.1%
v 7
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5841
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1457
24.9%
G 1453
24.9%
s 1446
24.8%
A 1428
24.4%
W 24
 
0.4%
l 9
 
0.2%
r 8
 
0.1%
v 7
 
0.1%
O 2
 
< 0.1%
t 2
 
< 0.1%
Other values (3) 5
 
0.1%

HeatingQC
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Ex
741 
TA
428 
Gd
241 
Fa
 
49
Po
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowEx
2nd rowEx
3rd rowEx
4th rowGd
5th rowEx

Common Values

ValueCountFrequency (%)
Ex 741
50.8%
TA 428
29.3%
Gd 241
 
16.5%
Fa 49
 
3.4%
Po 1
 
0.1%

Length

2023-02-02T13:33:08.301920image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:08.393005image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
ex 741
50.8%
ta 428
29.3%
gd 241
 
16.5%
fa 49
 
3.4%
po 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
E 741
25.4%
x 741
25.4%
T 428
14.7%
A 428
14.7%
G 241
 
8.3%
d 241
 
8.3%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1888
64.7%
Lowercase Letter 1032
35.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 741
39.2%
T 428
22.7%
A 428
22.7%
G 241
 
12.8%
F 49
 
2.6%
P 1
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
x 741
71.8%
d 241
 
23.4%
a 49
 
4.7%
o 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 2920
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 741
25.4%
x 741
25.4%
T 428
14.7%
A 428
14.7%
G 241
 
8.3%
d 241
 
8.3%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 741
25.4%
x 741
25.4%
T 428
14.7%
A 428
14.7%
G 241
 
8.3%
d 241
 
8.3%
F 49
 
1.7%
a 49
 
1.7%
P 1
 
< 0.1%
o 1
 
< 0.1%

CentralAir
Boolean

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
True
1365 
False
 
95
ValueCountFrequency (%)
True 1365
93.5%
False 95
 
6.5%
2023-02-02T13:33:08.474802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

1stFlrSF
Real number (ℝ)

Distinct753
Distinct (%)51.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1162.6267
Minimum334
Maximum4692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:08.552813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile672.95
Q1882
median1087
Q31391.25
95-th percentile1831.25
Maximum4692
Range4358
Interquartile range (IQR)509.25

Descriptive statistics

Standard deviation386.58774
Coefficient of variation (CV)0.33251235
Kurtosis5.7458415
Mean1162.6267
Median Absolute Deviation (MAD)234.5
Skewness1.3767566
Sum1697435
Variance149450.08
MonotonicityNot monotonic
2023-02-02T13:33:08.647979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 25
 
1.7%
1040 16
 
1.1%
912 14
 
1.0%
894 12
 
0.8%
848 12
 
0.8%
672 11
 
0.8%
630 9
 
0.6%
816 9
 
0.6%
483 7
 
0.5%
960 7
 
0.5%
Other values (743) 1338
91.6%
ValueCountFrequency (%)
334 1
 
0.1%
372 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
483 7
0.5%
495 1
 
0.1%
520 5
0.3%
525 1
 
0.1%
526 1
 
0.1%
536 1
 
0.1%
ValueCountFrequency (%)
4692 1
0.1%
3228 1
0.1%
3138 1
0.1%
2898 1
0.1%
2633 1
0.1%
2524 1
0.1%
2515 1
0.1%
2444 1
0.1%
2411 1
0.1%
2402 1
0.1%

2ndFlrSF
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct417
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346.99247
Minimum0
Maximum2065
Zeros829
Zeros (%)56.8%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:08.746757image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3728
95-th percentile1141.05
Maximum2065
Range2065
Interquartile range (IQR)728

Descriptive statistics

Standard deviation436.52844
Coefficient of variation (CV)1.2580343
Kurtosis-0.55346356
Mean346.99247
Median Absolute Deviation (MAD)0
Skewness0.81302982
Sum506609
Variance190557.08
MonotonicityNot monotonic
2023-02-02T13:33:08.841116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 829
56.8%
728 10
 
0.7%
504 9
 
0.6%
546 8
 
0.5%
672 8
 
0.5%
600 7
 
0.5%
720 7
 
0.5%
896 6
 
0.4%
862 5
 
0.3%
780 5
 
0.3%
Other values (407) 566
38.8%
ValueCountFrequency (%)
0 829
56.8%
110 1
 
0.1%
167 1
 
0.1%
192 1
 
0.1%
208 1
 
0.1%
213 1
 
0.1%
220 1
 
0.1%
224 1
 
0.1%
240 2
 
0.1%
252 2
 
0.1%
ValueCountFrequency (%)
2065 1
0.1%
1872 1
0.1%
1818 1
0.1%
1796 1
0.1%
1611 1
0.1%
1589 1
0.1%
1540 1
0.1%
1538 1
0.1%
1523 1
0.1%
1519 1
0.1%

LowQualFinSF
Real number (ℝ)

Distinct24
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.8445205
Minimum0
Maximum572
Zeros1434
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:08.929867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum572
Range572
Interquartile range (IQR)0

Descriptive statistics

Standard deviation48.623081
Coefficient of variation (CV)8.3194303
Kurtosis83.234817
Mean5.8445205
Median Absolute Deviation (MAD)0
Skewness9.0113413
Sum8533
Variance2364.204
MonotonicityNot monotonic
2023-02-02T13:33:09.013199image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1434
98.2%
80 3
 
0.2%
360 2
 
0.1%
205 1
 
0.1%
479 1
 
0.1%
397 1
 
0.1%
514 1
 
0.1%
120 1
 
0.1%
481 1
 
0.1%
232 1
 
0.1%
Other values (14) 14
 
1.0%
ValueCountFrequency (%)
0 1434
98.2%
53 1
 
0.1%
80 3
 
0.2%
120 1
 
0.1%
144 1
 
0.1%
156 1
 
0.1%
205 1
 
0.1%
232 1
 
0.1%
234 1
 
0.1%
360 2
 
0.1%
ValueCountFrequency (%)
572 1
0.1%
528 1
0.1%
515 1
0.1%
514 1
0.1%
513 1
0.1%
481 1
0.1%
479 1
0.1%
473 1
0.1%
420 1
0.1%
397 1
0.1%

GrLivArea
Real number (ℝ)

Distinct861
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1515.4637
Minimum334
Maximum5642
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:09.106828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum334
5-th percentile848
Q11129.5
median1464
Q31776.75
95-th percentile2466.1
Maximum5642
Range5308
Interquartile range (IQR)647.25

Descriptive statistics

Standard deviation525.48038
Coefficient of variation (CV)0.34674561
Kurtosis4.8951206
Mean1515.4637
Median Absolute Deviation (MAD)326
Skewness1.3665604
Sum2212577
Variance276129.63
MonotonicityNot monotonic
2023-02-02T13:33:09.206026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
864 22
 
1.5%
1040 14
 
1.0%
894 11
 
0.8%
1456 10
 
0.7%
848 10
 
0.7%
1200 9
 
0.6%
912 9
 
0.6%
816 8
 
0.5%
1092 8
 
0.5%
1728 7
 
0.5%
Other values (851) 1352
92.6%
ValueCountFrequency (%)
334 1
 
0.1%
438 1
 
0.1%
480 1
 
0.1%
520 1
 
0.1%
605 1
 
0.1%
616 1
 
0.1%
630 6
0.4%
672 2
 
0.1%
691 1
 
0.1%
693 1
 
0.1%
ValueCountFrequency (%)
5642 1
0.1%
4676 1
0.1%
4476 1
0.1%
4316 1
0.1%
3627 1
0.1%
3608 1
0.1%
3493 1
0.1%
3447 1
0.1%
3395 1
0.1%
3279 1
0.1%

BsmtFullBath
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
856 
1
588 
2
 
15
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Length

2023-02-02T13:33:09.299073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:09.389697image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 856
58.6%
1 588
40.3%
2 15
 
1.0%
3 1
 
0.1%

BsmtHalfBath
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
1378 
1
 
80
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Length

2023-02-02T13:33:09.467057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:09.554370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1378
94.4%
1 80
 
5.5%
2 2
 
0.1%

FullBath
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2
768 
1
650 
3
 
33
0
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Length

2023-02-02T13:33:09.627110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:09.718277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring characters

ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 768
52.6%
1 650
44.5%
3 33
 
2.3%
0 9
 
0.6%

HalfBath
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
913 
1
535 
2
 
12

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Length

2023-02-02T13:33:09.794291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:09.882175image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 913
62.5%
1 535
36.6%
2 12
 
0.8%

BedroomAbvGr
Real number (ℝ)

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8664384
Minimum0
Maximum8
Zeros6
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:09.950114image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q33
95-th percentile4
Maximum8
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.81577804
Coefficient of variation (CV)0.2845964
Kurtosis2.2308746
Mean2.8664384
Median Absolute Deviation (MAD)0
Skewness0.2117901
Sum4185
Variance0.66549382
MonotonicityNot monotonic
2023-02-02T13:33:10.026194image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 804
55.1%
2 358
24.5%
4 213
 
14.6%
1 50
 
3.4%
5 21
 
1.4%
6 7
 
0.5%
0 6
 
0.4%
8 1
 
0.1%
ValueCountFrequency (%)
0 6
 
0.4%
1 50
 
3.4%
2 358
24.5%
3 804
55.1%
4 213
 
14.6%
5 21
 
1.4%
6 7
 
0.5%
8 1
 
0.1%
ValueCountFrequency (%)
8 1
 
0.1%
6 7
 
0.5%
5 21
 
1.4%
4 213
 
14.6%
3 804
55.1%
2 358
24.5%
1 50
 
3.4%
0 6
 
0.4%

KitchenAbvGr
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
1
1392 
2
 
65
3
 
2
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Length

2023-02-02T13:33:10.112982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:10.201550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1392
95.3%
2 65
 
4.5%
3 2
 
0.1%
0 1
 
0.1%

KitchenQual
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
TA
735 
Gd
586 
Ex
100 
Fa
 
39

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2920
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGd
2nd rowTA
3rd rowGd
4th rowGd
5th rowGd

Common Values

ValueCountFrequency (%)
TA 735
50.3%
Gd 586
40.1%
Ex 100
 
6.8%
Fa 39
 
2.7%

Length

2023-02-02T13:33:10.276220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:10.366130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
ta 735
50.3%
gd 586
40.1%
ex 100
 
6.8%
fa 39
 
2.7%

Most occurring characters

ValueCountFrequency (%)
T 735
25.2%
A 735
25.2%
G 586
20.1%
d 586
20.1%
E 100
 
3.4%
x 100
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2195
75.2%
Lowercase Letter 725
 
24.8%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 735
33.5%
A 735
33.5%
G 586
26.7%
E 100
 
4.6%
F 39
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
d 586
80.8%
x 100
 
13.8%
a 39
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 2920
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 735
25.2%
A 735
25.2%
G 586
20.1%
d 586
20.1%
E 100
 
3.4%
x 100
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2920
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 735
25.2%
A 735
25.2%
G 586
20.1%
d 586
20.1%
E 100
 
3.4%
x 100
 
3.4%
F 39
 
1.3%
a 39
 
1.3%

TotRmsAbvGrd
Real number (ℝ)

Distinct12
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5178082
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:10.433783image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q15
median6
Q37
95-th percentile10
Maximum14
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6253933
Coefficient of variation (CV)0.24937728
Kurtosis0.88076157
Mean6.5178082
Median Absolute Deviation (MAD)1
Skewness0.67634084
Sum9516
Variance2.6419033
MonotonicityNot monotonic
2023-02-02T13:33:10.513321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
6 402
27.5%
7 329
22.5%
5 275
18.8%
8 187
12.8%
4 97
 
6.6%
9 75
 
5.1%
10 47
 
3.2%
11 18
 
1.2%
3 17
 
1.2%
12 11
 
0.8%
Other values (2) 2
 
0.1%
ValueCountFrequency (%)
2 1
 
0.1%
3 17
 
1.2%
4 97
 
6.6%
5 275
18.8%
6 402
27.5%
7 329
22.5%
8 187
12.8%
9 75
 
5.1%
10 47
 
3.2%
11 18
 
1.2%
ValueCountFrequency (%)
14 1
 
0.1%
12 11
 
0.8%
11 18
 
1.2%
10 47
 
3.2%
9 75
 
5.1%
8 187
12.8%
7 329
22.5%
6 402
27.5%
5 275
18.8%
4 97
 
6.6%

Functional
Categorical

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Typ
1360 
Min2
 
34
Min1
 
31
Mod
 
15
Maj1
 
14
Other values (2)
 
6

Length

Max length4
Median length3
Mean length3.0575342
Min length3

Characters and Unicode

Total characters4464
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowTyp
2nd rowTyp
3rd rowTyp
4th rowTyp
5th rowTyp

Common Values

ValueCountFrequency (%)
Typ 1360
93.2%
Min2 34
 
2.3%
Min1 31
 
2.1%
Mod 15
 
1.0%
Maj1 14
 
1.0%
Maj2 5
 
0.3%
Sev 1
 
0.1%

Length

2023-02-02T13:33:10.600631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:10.695277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
typ 1360
93.2%
min2 34
 
2.3%
min1 31
 
2.1%
mod 15
 
1.0%
maj1 14
 
1.0%
maj2 5
 
0.3%
sev 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
T 1360
30.5%
y 1360
30.5%
p 1360
30.5%
M 99
 
2.2%
i 65
 
1.5%
n 65
 
1.5%
1 45
 
1.0%
2 39
 
0.9%
a 19
 
0.4%
j 19
 
0.4%
Other values (5) 33
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2920
65.4%
Uppercase Letter 1460
32.7%
Decimal Number 84
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
y 1360
46.6%
p 1360
46.6%
i 65
 
2.2%
n 65
 
2.2%
a 19
 
0.7%
j 19
 
0.7%
o 15
 
0.5%
d 15
 
0.5%
e 1
 
< 0.1%
v 1
 
< 0.1%
Uppercase Letter
ValueCountFrequency (%)
T 1360
93.2%
M 99
 
6.8%
S 1
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 45
53.6%
2 39
46.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 4380
98.1%
Common 84
 
1.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1360
31.1%
y 1360
31.1%
p 1360
31.1%
M 99
 
2.3%
i 65
 
1.5%
n 65
 
1.5%
a 19
 
0.4%
j 19
 
0.4%
o 15
 
0.3%
d 15
 
0.3%
Other values (3) 3
 
0.1%
Common
ValueCountFrequency (%)
1 45
53.6%
2 39
46.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4464
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 1360
30.5%
y 1360
30.5%
p 1360
30.5%
M 99
 
2.2%
i 65
 
1.5%
n 65
 
1.5%
1 45
 
1.0%
2 39
 
0.9%
a 19
 
0.4%
j 19
 
0.4%
Other values (5) 33
 
0.7%

Fireplaces
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
0
690 
1
650 
2
115 
3
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Length

2023-02-02T13:33:10.775817image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:10.864766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 690
47.3%
1 650
44.5%
2 115
 
7.9%
3 5
 
0.3%

GarageCars
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
2
824 
1
369 
3
181 
0
 
81
4
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row3
5th row3

Common Values

ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Length

2023-02-02T13:33:10.940386image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:11.032062image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1460
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1460
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 824
56.4%
1 369
25.3%
3 181
 
12.4%
0 81
 
5.5%
4 5
 
0.3%

GarageArea
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct441
Distinct (%)30.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean472.98014
Minimum0
Maximum1418
Zeros81
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:11.118855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1334.5
median480
Q3576
95-th percentile850.1
Maximum1418
Range1418
Interquartile range (IQR)241.5

Descriptive statistics

Standard deviation213.80484
Coefficient of variation (CV)0.45203768
Kurtosis0.9170672
Mean472.98014
Median Absolute Deviation (MAD)120
Skewness0.17998091
Sum690551
Variance45712.51
MonotonicityNot monotonic
2023-02-02T13:33:11.215913image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 81
 
5.5%
440 49
 
3.4%
576 47
 
3.2%
240 38
 
2.6%
484 34
 
2.3%
528 33
 
2.3%
288 27
 
1.8%
400 25
 
1.7%
264 24
 
1.6%
480 24
 
1.6%
Other values (431) 1078
73.8%
ValueCountFrequency (%)
0 81
5.5%
160 2
 
0.1%
164 1
 
0.1%
180 9
 
0.6%
186 1
 
0.1%
189 1
 
0.1%
192 1
 
0.1%
198 1
 
0.1%
200 4
 
0.3%
205 3
 
0.2%
ValueCountFrequency (%)
1418 1
0.1%
1390 1
0.1%
1356 1
0.1%
1248 1
0.1%
1220 1
0.1%
1166 1
0.1%
1134 1
0.1%
1069 1
0.1%
1053 1
0.1%
1052 2
0.1%

PavedDrive
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.5 KiB
Y
1340 
N
 
90
P
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1460
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowY
2nd rowY
3rd rowY
4th rowY
5th rowY

Common Values

ValueCountFrequency (%)
Y 1340
91.8%
N 90
 
6.2%
P 30
 
2.1%

Length

2023-02-02T13:33:11.312107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-02T13:33:11.400391image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
y 1340
91.8%
n 90
 
6.2%
p 30
 
2.1%

Most occurring characters

ValueCountFrequency (%)
Y 1340
91.8%
N 90
 
6.2%
P 30
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1460
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y 1340
91.8%
N 90
 
6.2%
P 30
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1460
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y 1340
91.8%
N 90
 
6.2%
P 30
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
Y 1340
91.8%
N 90
 
6.2%
P 30
 
2.1%

WoodDeckSF
Real number (ℝ)

Distinct274
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.244521
Minimum0
Maximum857
Zeros761
Zeros (%)52.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:11.480381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3168
95-th percentile335
Maximum857
Range857
Interquartile range (IQR)168

Descriptive statistics

Standard deviation125.33879
Coefficient of variation (CV)1.3299319
Kurtosis2.9929509
Mean94.244521
Median Absolute Deviation (MAD)0
Skewness1.5413758
Sum137597
Variance15709.813
MonotonicityNot monotonic
2023-02-02T13:33:11.577877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 761
52.1%
192 38
 
2.6%
100 36
 
2.5%
144 33
 
2.3%
120 31
 
2.1%
168 28
 
1.9%
140 15
 
1.0%
224 14
 
1.0%
208 10
 
0.7%
240 10
 
0.7%
Other values (264) 484
33.2%
ValueCountFrequency (%)
0 761
52.1%
12 2
 
0.1%
24 2
 
0.1%
26 2
 
0.1%
28 2
 
0.1%
30 1
 
0.1%
32 1
 
0.1%
33 1
 
0.1%
35 1
 
0.1%
36 4
 
0.3%
ValueCountFrequency (%)
857 1
0.1%
736 1
0.1%
728 1
0.1%
670 1
0.1%
668 1
0.1%
635 1
0.1%
586 1
0.1%
576 1
0.1%
574 1
0.1%
550 1
0.1%

OpenPorchSF
Real number (ℝ)

Distinct202
Distinct (%)13.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.660274
Minimum0
Maximum547
Zeros656
Zeros (%)44.9%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:11.674707image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median25
Q368
95-th percentile175.05
Maximum547
Range547
Interquartile range (IQR)68

Descriptive statistics

Standard deviation66.256028
Coefficient of variation (CV)1.4199665
Kurtosis8.4903358
Mean46.660274
Median Absolute Deviation (MAD)25
Skewness2.3643417
Sum68124
Variance4389.8612
MonotonicityNot monotonic
2023-02-02T13:33:11.769982image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 656
44.9%
36 29
 
2.0%
48 22
 
1.5%
20 21
 
1.4%
40 19
 
1.3%
45 19
 
1.3%
24 16
 
1.1%
30 16
 
1.1%
60 15
 
1.0%
39 14
 
1.0%
Other values (192) 633
43.4%
ValueCountFrequency (%)
0 656
44.9%
4 1
 
0.1%
8 1
 
0.1%
10 1
 
0.1%
11 1
 
0.1%
12 3
 
0.2%
15 1
 
0.1%
16 8
 
0.5%
17 2
 
0.1%
18 5
 
0.3%
ValueCountFrequency (%)
547 1
0.1%
523 1
0.1%
502 1
0.1%
418 1
0.1%
406 1
0.1%
364 1
0.1%
341 1
0.1%
319 1
0.1%
312 2
0.1%
304 1
0.1%

EnclosedPorch
Real number (ℝ)

Distinct120
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.95411
Minimum0
Maximum552
Zeros1252
Zeros (%)85.8%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:11.869238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile180.15
Maximum552
Range552
Interquartile range (IQR)0

Descriptive statistics

Standard deviation61.119149
Coefficient of variation (CV)2.7839502
Kurtosis10.430766
Mean21.95411
Median Absolute Deviation (MAD)0
Skewness3.0898719
Sum32053
Variance3735.5503
MonotonicityNot monotonic
2023-02-02T13:33:11.964137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1252
85.8%
112 15
 
1.0%
96 6
 
0.4%
192 5
 
0.3%
144 5
 
0.3%
120 5
 
0.3%
216 5
 
0.3%
156 4
 
0.3%
116 4
 
0.3%
252 4
 
0.3%
Other values (110) 155
 
10.6%
ValueCountFrequency (%)
0 1252
85.8%
19 1
 
0.1%
20 1
 
0.1%
24 1
 
0.1%
30 1
 
0.1%
32 2
 
0.1%
34 2
 
0.1%
36 2
 
0.1%
37 1
 
0.1%
39 2
 
0.1%
ValueCountFrequency (%)
552 1
0.1%
386 1
0.1%
330 1
0.1%
318 1
0.1%
301 1
0.1%
294 1
0.1%
293 1
0.1%
291 1
0.1%
286 1
0.1%
280 1
0.1%

3SsnPorch
Real number (ℝ)

Distinct20
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.409589
Minimum0
Maximum508
Zeros1436
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:12.054021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum508
Range508
Interquartile range (IQR)0

Descriptive statistics

Standard deviation29.317331
Coefficient of variation (CV)8.5984939
Kurtosis123.66238
Mean3.409589
Median Absolute Deviation (MAD)0
Skewness10.304342
Sum4978
Variance859.50587
MonotonicityNot monotonic
2023-02-02T13:33:12.139238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 1436
98.4%
168 3
 
0.2%
144 2
 
0.1%
180 2
 
0.1%
216 2
 
0.1%
290 1
 
0.1%
153 1
 
0.1%
96 1
 
0.1%
23 1
 
0.1%
162 1
 
0.1%
Other values (10) 10
 
0.7%
ValueCountFrequency (%)
0 1436
98.4%
23 1
 
0.1%
96 1
 
0.1%
130 1
 
0.1%
140 1
 
0.1%
144 2
 
0.1%
153 1
 
0.1%
162 1
 
0.1%
168 3
 
0.2%
180 2
 
0.1%
ValueCountFrequency (%)
508 1
0.1%
407 1
0.1%
320 1
0.1%
304 1
0.1%
290 1
0.1%
245 1
0.1%
238 1
0.1%
216 2
0.1%
196 1
0.1%
182 1
0.1%

ScreenPorch
Real number (ℝ)

Distinct76
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.060959
Minimum0
Maximum480
Zeros1344
Zeros (%)92.1%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:12.232260image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile160
Maximum480
Range480
Interquartile range (IQR)0

Descriptive statistics

Standard deviation55.757415
Coefficient of variation (CV)3.7021159
Kurtosis18.439068
Mean15.060959
Median Absolute Deviation (MAD)0
Skewness4.1222137
Sum21989
Variance3108.8894
MonotonicityNot monotonic
2023-02-02T13:33:12.333805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1344
92.1%
192 6
 
0.4%
120 5
 
0.3%
224 5
 
0.3%
189 4
 
0.3%
180 4
 
0.3%
147 3
 
0.2%
90 3
 
0.2%
160 3
 
0.2%
144 3
 
0.2%
Other values (66) 80
 
5.5%
ValueCountFrequency (%)
0 1344
92.1%
40 1
 
0.1%
53 1
 
0.1%
60 1
 
0.1%
63 1
 
0.1%
80 1
 
0.1%
90 3
 
0.2%
95 1
 
0.1%
99 1
 
0.1%
100 2
 
0.1%
ValueCountFrequency (%)
480 1
0.1%
440 1
0.1%
410 1
0.1%
396 1
0.1%
385 1
0.1%
374 1
0.1%
322 1
0.1%
312 1
0.1%
291 1
0.1%
288 2
0.1%

PoolArea
Real number (ℝ)

Distinct8
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7589041
Minimum0
Maximum738
Zeros1453
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:12.420703image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum738
Range738
Interquartile range (IQR)0

Descriptive statistics

Standard deviation40.177307
Coefficient of variation (CV)14.562778
Kurtosis223.2685
Mean2.7589041
Median Absolute Deviation (MAD)0
Skewness14.828374
Sum4028
Variance1614.216
MonotonicityNot monotonic
2023-02-02T13:33:12.496082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 1453
99.5%
512 1
 
0.1%
648 1
 
0.1%
576 1
 
0.1%
555 1
 
0.1%
480 1
 
0.1%
519 1
 
0.1%
738 1
 
0.1%
ValueCountFrequency (%)
0 1453
99.5%
480 1
 
0.1%
512 1
 
0.1%
519 1
 
0.1%
555 1
 
0.1%
576 1
 
0.1%
648 1
 
0.1%
738 1
 
0.1%
ValueCountFrequency (%)
738 1
 
0.1%
648 1
 
0.1%
576 1
 
0.1%
555 1
 
0.1%
519 1
 
0.1%
512 1
 
0.1%
480 1
 
0.1%
0 1453
99.5%

MiscVal
Real number (ℝ)

SKEWED  ZEROS 

Distinct21
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.489041
Minimum0
Maximum15500
Zeros1408
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:12.580356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15500
Range15500
Interquartile range (IQR)0

Descriptive statistics

Standard deviation496.12302
Coefficient of variation (CV)11.408001
Kurtosis701.00334
Mean43.489041
Median Absolute Deviation (MAD)0
Skewness24.476794
Sum63494
Variance246138.06
MonotonicityNot monotonic
2023-02-02T13:33:12.664360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 1408
96.4%
400 11
 
0.8%
500 8
 
0.5%
700 5
 
0.3%
450 4
 
0.3%
600 4
 
0.3%
2000 4
 
0.3%
1200 2
 
0.1%
480 2
 
0.1%
15500 1
 
0.1%
Other values (11) 11
 
0.8%
ValueCountFrequency (%)
0 1408
96.4%
54 1
 
0.1%
350 1
 
0.1%
400 11
 
0.8%
450 4
 
0.3%
480 2
 
0.1%
500 8
 
0.5%
560 1
 
0.1%
600 4
 
0.3%
620 1
 
0.1%
ValueCountFrequency (%)
15500 1
 
0.1%
8300 1
 
0.1%
3500 1
 
0.1%
2500 1
 
0.1%
2000 4
0.3%
1400 1
 
0.1%
1300 1
 
0.1%
1200 2
0.1%
1150 1
 
0.1%
800 1
 
0.1%

SalePrice
Real number (ℝ)

Distinct663
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180921.2
Minimum34900
Maximum755000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:12.762758image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum34900
5-th percentile88000
Q1129975
median163000
Q3214000
95-th percentile326100
Maximum755000
Range720100
Interquartile range (IQR)84025

Descriptive statistics

Standard deviation79442.503
Coefficient of variation (CV)0.43910003
Kurtosis6.5362819
Mean180921.2
Median Absolute Deviation (MAD)38000
Skewness1.8828758
Sum2.6414495 × 108
Variance6.3111113 × 109
MonotonicityNot monotonic
2023-02-02T13:33:12.861948image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140000 20
 
1.4%
135000 17
 
1.2%
155000 14
 
1.0%
145000 14
 
1.0%
190000 13
 
0.9%
110000 13
 
0.9%
115000 12
 
0.8%
160000 12
 
0.8%
130000 11
 
0.8%
139000 11
 
0.8%
Other values (653) 1323
90.6%
ValueCountFrequency (%)
34900 1
0.1%
35311 1
0.1%
37900 1
0.1%
39300 1
0.1%
40000 1
0.1%
52000 1
0.1%
52500 1
0.1%
55000 2
0.1%
55993 1
0.1%
58500 1
0.1%
ValueCountFrequency (%)
755000 1
0.1%
745000 1
0.1%
625000 1
0.1%
611657 1
0.1%
582933 1
0.1%
556581 1
0.1%
555000 1
0.1%
538000 1
0.1%
501837 1
0.1%
485000 1
0.1%

Built_to_Sale
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct122
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.547945
Minimum0
Maximum136
Zeros64
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:12.962410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median35
Q354
95-th percentile91
Maximum136
Range136
Interquartile range (IQR)46

Descriptive statistics

Standard deviation30.250152
Coefficient of variation (CV)0.82768408
Kurtosis-0.43682934
Mean36.547945
Median Absolute Deviation (MAD)25
Skewness0.60903023
Sum53360
Variance915.07172
MonotonicityNot monotonic
2023-02-02T13:33:13.070837image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 100
 
6.8%
0 64
 
4.4%
4 41
 
2.8%
2 39
 
2.7%
3 36
 
2.5%
31 31
 
2.1%
5 30
 
2.1%
6 30
 
2.1%
8 25
 
1.7%
37 25
 
1.7%
Other values (112) 1039
71.2%
ValueCountFrequency (%)
0 64
4.4%
1 100
6.8%
2 39
 
2.7%
3 36
 
2.5%
4 41
2.8%
5 30
 
2.1%
6 30
 
2.1%
7 24
 
1.6%
8 25
 
1.7%
9 23
 
1.6%
ValueCountFrequency (%)
136 1
0.1%
135 1
0.1%
129 1
0.1%
128 1
0.1%
127 2
0.1%
126 1
0.1%
125 1
0.1%
122 1
0.1%
119 1
0.1%
117 2
0.1%

Years_Since_Remod
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.950685
Minimum0
Maximum60
Zeros125
Zeros (%)8.6%
Negative0
Negative (%)0.0%
Memory size11.5 KiB
2023-02-02T13:33:13.190654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median14
Q341
95-th percentile58
Maximum60
Range60
Interquartile range (IQR)37

Descriptive statistics

Standard deviation20.639875
Coefficient of variation (CV)0.8993141
Kurtosis-1.266028
Mean22.950685
Median Absolute Deviation (MAD)13
Skewness0.50257501
Sum33508
Variance426.00442
MonotonicityNot monotonic
2023-02-02T13:33:13.290040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 125
 
8.6%
1 87
 
6.0%
4 63
 
4.3%
3 53
 
3.6%
2 52
 
3.6%
56 52
 
3.6%
5 46
 
3.2%
57 43
 
2.9%
8 42
 
2.9%
6 40
 
2.7%
Other values (51) 857
58.7%
ValueCountFrequency (%)
0 125
8.6%
1 87
6.0%
2 52
3.6%
3 53
3.6%
4 63
4.3%
5 46
 
3.2%
6 40
 
2.7%
7 38
 
2.6%
8 42
 
2.9%
9 38
 
2.6%
ValueCountFrequency (%)
60 19
 
1.3%
59 39
2.7%
58 37
2.5%
57 43
2.9%
56 52
3.6%
55 5
 
0.3%
54 9
 
0.6%
53 16
 
1.1%
52 10
 
0.7%
51 14
 
1.0%

Interactions

2023-02-02T13:33:00.154246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:01.246650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:03.475455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:05.614242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:07.832822image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:10.088210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:12.445020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:14.632862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:16.878295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:19.026402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:21.247434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:25.474120image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:27.624590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:29.876416image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:32.013976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:34.285402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:36.491983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:38.740815image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:40.922224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:43.176951image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:45.393766image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:47.560185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:49.731171image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:53.272173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:55.613839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:57.894394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:33:00.245101image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:01.333198image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:03.558829image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:05.696230image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:07.920844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:10.175994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:12.534494image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:14.719176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:16.961255image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:19.114222image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:21.328932image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:25.555741image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:27.709143image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:29.959425image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:32.100809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:34.371531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:36.582942image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:38.827277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:41.005881image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:43.264135image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:45.477665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:47.644073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:49.819165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:53.364083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:55.706365image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:57.985461image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:33:00.333347image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:01.414683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:03.637197image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:05.774675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:08.006445image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:10.260934image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:12.616455image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:14.802276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:17.042446image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:19.196124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:21.407221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:25.638002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:27.790873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:30.038148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:32.184208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:34.453717image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:36.665116image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:38.906769image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:41.085753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:43.345760image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:45.557450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:47.723709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:49.901514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:53.456356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:55.790350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:58.069102image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:33:00.423374image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:01.497029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:03.715403image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:05.852498image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:08.090632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:10.344427image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:12.696510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:14.885319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:17.123433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:19.281048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:21.485896image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:25.717140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:27.871603image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:30.117130image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:32.267536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:34.535857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:36.747112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:38.985186image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:41.165571image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:43.426453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:45.637666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:47.803729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:49.983886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:53.543390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:55.874266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:58.151159image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:33:00.519146image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:01.585087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:03.801147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:05.937389image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:08.181311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:10.435797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:12.784774image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:14.975344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:17.209402image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:19.370357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:21.570096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:25.802762image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:27.961417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:30.203373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:32.357816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:34.624990image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:36.837468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:39.071989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:41.252992image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:43.515568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:45.724514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2023-02-02T13:32:11.869422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:14.115701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:16.348406image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:18.520218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:20.719861image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:24.971011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:27.123539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:29.353468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:31.507221image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:33.749582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:35.972475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:38.213110image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:40.386072image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:42.663150image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:44.880867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:47.048173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:49.219542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:51.440777image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:55.074049image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:57.362434image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:59.613867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:33:01.958687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:03.034154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:05.186695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:07.404886image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:09.642373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:11.968426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:14.197572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:16.433844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:18.601011image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:20.803300image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:25.051754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:27.202989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:29.436714image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:31.587870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:33.835626image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:36.055311image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:38.298149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:40.473278image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:42.745862image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:44.962839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:47.130462image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:49.300357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:51.524651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:55.160605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:57.447908image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:59.698415image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:33:02.050364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:03.120954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:05.272039image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:07.490854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:09.730420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:12.059349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:14.284568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:16.522234image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:18.685730image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:20.894149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:25.135276image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:27.286611image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:29.523454image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:31.672127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:33.924852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:36.142710image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:38.386183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:40.565414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:42.831246image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:45.048316image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:47.215945image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:49.386010image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:52.898628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:55.251033image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:57.536457image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:59.787984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:33:02.138854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:03.209722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:05.358214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:07.579433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:09.821426image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:12.154762image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:14.373589image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:16.612642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:18.772245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:20.983327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:25.223823image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:27.373711image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:29.616345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:31.758459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:34.015838image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:36.231000image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:38.476306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:40.657250image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:42.919813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:45.136339image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:47.303055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:49.474153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:52.996679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:55.343987image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:57.627831image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:59.877892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:33:02.226595image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:03.297172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:05.445429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:07.665309image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:09.911983image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:12.254509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:14.461303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:16.702252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:18.858359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:21.072379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:25.309094image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:27.458899image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:29.704882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:31.843729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:34.106619image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:36.319240image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:38.566006image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:40.749325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:43.005940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:45.222975image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:47.390381image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:49.561272image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:53.087283image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:55.435452image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:57.717343image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:59.966956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:33:02.313313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:03.386218image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:05.529991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:07.749349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:10.000748image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:12.350599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:14.547360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:16.790898image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:18.942504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:21.161397image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:25.392615image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:27.542459image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:29.791055image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:31.929357image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:34.195518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:36.406901image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:38.654048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:40.838565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:43.092215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:45.309873image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:47.475510image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:49.646616image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:53.184795image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:55.525449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:32:57.806513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-02-02T13:33:00.056725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-02-02T13:33:13.441474image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
MSSubClassLotAreaOverallQualOverallCondMasVnrAreaBsmtFinSF1BsmtFinSF2BsmtUnfSFTotalBsmtSF1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBedroomAbvGrTotRmsAbvGrdGarageAreaWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValSalePriceBuilt_to_SaleYears_Since_RemodMSZoningStreetLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleRoofStyleRoofMatlExterior1stExterior2ndExterQualExterCondFoundationHeatingHeatingQCCentralAirBsmtFullBathBsmtHalfBathFullBathHalfBathKitchenAbvGrKitchenQualFunctionalFireplacesGarageCarsPavedDrive
MSSubClass1.000-0.2700.108-0.0720.026-0.108-0.084-0.118-0.319-0.2780.4880.0760.2040.0690.166-0.0470.0230.0320.011-0.036-0.0220.033-0.0330.007-0.040-0.0140.2640.1030.1380.0840.0000.0620.0000.4220.1030.1030.8510.6170.1170.0390.1880.2100.2430.1150.2630.1050.1680.2540.2090.0860.2490.5120.4760.2260.0830.1930.2430.176
LotArea-0.2701.0000.233-0.0470.1760.1720.0720.0780.3660.4440.119-0.0200.4490.3380.4060.3670.1840.177-0.0670.0620.0920.0840.0590.456-0.105-0.0780.0000.2900.2660.2560.0000.0790.4490.1620.0000.0000.0360.0000.1120.2520.0260.0710.0000.0000.0000.0840.0000.0000.2110.0000.0980.0000.0000.0000.0140.1600.0110.030
OverallQual0.1080.2331.000-0.1780.4080.133-0.1180.2730.4600.4090.290-0.0340.6030.1220.4280.5420.2590.435-0.1620.0330.0460.057-0.0880.810-0.644-0.5630.1900.0730.1160.1610.0000.0170.1520.3210.0610.1530.1310.1440.1170.0990.2000.1930.6140.1950.2910.1670.2590.3740.0660.0620.4040.2250.1060.5400.1150.2670.4020.175
OverallCond-0.072-0.047-0.1781.000-0.176-0.0110.102-0.128-0.217-0.1670.0010.040-0.154-0.004-0.105-0.201-0.043-0.1330.1100.0320.075-0.0060.087-0.1290.4190.0520.1610.0680.0600.1010.0000.0000.1880.2220.0510.0990.1280.1220.0440.0000.1890.1690.3190.3790.2560.0920.1780.3150.0000.1020.3090.0790.0730.2470.1670.1050.2190.186
MasVnrArea0.0260.1760.408-0.1761.0000.238-0.0600.0750.3560.3490.062-0.1060.3190.1130.2620.3610.1740.205-0.1790.0420.0390.005-0.0490.416-0.391-0.2290.0610.0000.0700.0250.1700.0360.0000.1820.0000.1030.0000.0460.1050.1420.0000.0260.2400.0000.0750.0000.0350.1050.0250.0000.1790.1360.0000.1870.0000.1540.2000.074
BsmtFinSF1-0.1080.1720.133-0.0110.2381.0000.050-0.5740.4100.323-0.191-0.0790.057-0.084-0.0500.2440.1790.081-0.1480.0470.0720.0580.0050.302-0.185-0.0560.0910.0190.2060.1380.0000.0450.0830.2010.1070.3120.0000.1140.0960.4490.1380.1390.2080.0000.1110.0000.0590.1520.3970.0280.1580.0120.0000.2090.0000.2980.1760.109
BsmtFinSF2-0.0840.072-0.1180.102-0.0600.0501.000-0.2710.0700.067-0.1020.002-0.0520.010-0.059-0.0070.069-0.0690.042-0.0160.0590.0680.030-0.0390.1130.1310.0000.0490.0560.0490.1840.0000.1410.1230.0000.0390.0000.0000.1340.1540.0720.0630.0410.0000.0700.0000.0000.0000.0880.0880.0360.0080.0000.0400.1200.0800.0000.000
BsmtUnfSF-0.1180.0780.273-0.1280.075-0.574-0.2711.0000.3290.2240.0600.0200.2530.1580.2610.109-0.0350.1560.0440.013-0.012-0.037-0.0440.185-0.141-0.1870.0720.0000.0390.0640.0000.0120.0530.1910.0160.0620.1220.1500.0910.0000.0940.1020.2510.0260.1700.0000.0980.0600.2650.0540.1870.1220.0620.1930.0540.0870.1790.095
TotalBsmtSF-0.3190.3660.460-0.2170.3560.4100.0700.3291.0000.829-0.286-0.0810.3710.0590.2340.4870.2310.270-0.1720.0490.0890.047-0.0610.603-0.423-0.3020.1190.0000.2000.1060.0000.0290.0000.2370.0830.1440.1200.1640.1230.4240.1340.1440.3190.0380.2330.0730.1400.2230.2040.0000.2360.0970.0700.2960.0000.3200.2600.124
1stFlrSF-0.2780.4440.409-0.1670.3490.3230.0670.2240.8291.000-0.276-0.0390.4940.1410.3620.4900.2190.235-0.1290.0600.1080.071-0.0330.575-0.290-0.2430.1600.0000.2070.0960.0000.0570.0000.2410.0860.1910.1840.1600.1500.3940.1820.1550.2710.0000.0880.0000.0940.1440.1910.0000.2580.1210.0410.2510.0340.3410.2380.104
2ndFlrSF0.4880.1190.2900.0010.062-0.191-0.1020.060-0.286-0.2761.0000.0580.6430.5100.5870.0980.0710.2250.046-0.0230.0120.061-0.0050.294-0.035-0.0800.1590.0000.1480.0580.0000.0520.0000.2500.0520.1100.1250.4350.1200.1060.1010.1320.2090.0320.1570.0430.1120.0360.1250.0000.4070.4490.0000.1830.0000.1650.2280.112
LowQualFinSF0.076-0.020-0.0340.040-0.106-0.0790.0020.020-0.081-0.0390.0581.0000.0640.0210.042-0.048-0.0420.0100.0480.022-0.0190.0660.029-0.0680.1430.0590.1450.0000.0000.0730.0000.0000.0530.1120.0000.0840.0760.2630.0000.0490.0000.0000.0900.0870.0270.3080.0590.1260.0000.0000.0000.0000.0000.0600.0730.0000.0870.092
GrLivArea0.2040.4490.603-0.1540.3190.057-0.0520.2530.3710.4940.6430.0641.0000.5430.8280.4680.2270.398-0.0490.0340.0860.068-0.0490.731-0.289-0.2900.1060.0000.2220.1000.0000.0460.0360.2090.0870.2710.0490.2580.0620.4060.1110.1230.2860.0530.1520.0520.1430.1580.1360.0000.4680.3000.0000.2660.0000.3760.2870.094
BedroomAbvGr0.0690.3380.122-0.0040.113-0.0840.0100.1580.0590.1410.5100.0210.5431.0000.6680.1120.0560.1000.002-0.0190.0340.0720.0130.2350.0310.0480.1650.0000.0330.1100.0000.0000.1000.2060.0600.0000.3020.2420.1400.1010.0820.0670.1710.0000.0860.0610.0210.1600.2550.0300.4480.2500.2330.1310.0250.1070.1340.098
TotRmsAbvGrd0.1660.4060.428-0.1050.262-0.050-0.0590.2610.2340.3620.5870.0420.8280.6681.0000.3310.1650.285-0.029-0.0030.0320.059-0.0210.533-0.177-0.2060.1750.0000.0900.0730.0000.0000.0610.2040.0770.1270.1980.2660.1220.1300.0980.0990.2740.0000.1180.0360.0990.1120.0630.0000.3890.2700.1740.2380.0000.2230.2420.095
GarageArea-0.0470.3670.542-0.2010.3610.244-0.0070.1090.4870.4900.098-0.0480.4680.1120.3311.0000.2480.338-0.1780.0360.0290.042-0.0360.649-0.525-0.4000.1890.2600.1560.1090.0000.0420.0400.2590.0650.1540.1410.1220.0770.2110.1420.1390.3440.1140.1890.0670.1410.2760.1370.0250.2790.1600.0920.3340.0000.2280.7590.271
WoodDeckSF0.0230.1840.259-0.0430.1740.1790.069-0.0350.2310.2190.071-0.0420.2270.0560.1650.2481.0000.124-0.158-0.028-0.0900.0500.0170.354-0.286-0.2300.0690.2220.1010.1140.0000.0490.1380.1940.0580.0000.0960.0520.0550.1660.1020.1000.1750.0440.1380.0000.1210.1520.1900.0000.2480.0820.0000.1860.0760.1460.1480.074
OpenPorchSF0.0320.1770.435-0.1330.2050.081-0.0690.1560.2700.2350.2250.0100.3980.1000.2850.3380.1241.000-0.1690.0170.0070.037-0.0350.478-0.395-0.3610.1460.0350.0730.0000.0440.0000.0000.1200.0610.2800.0000.1270.0110.1090.0780.0810.1790.1600.1240.0750.1040.1010.0790.0000.1660.1490.0000.1590.0840.1220.1290.034
EnclosedPorch0.011-0.067-0.1620.110-0.179-0.1480.0420.044-0.172-0.1290.0460.048-0.0490.002-0.029-0.178-0.158-0.1691.000-0.039-0.0810.0040.039-0.2180.4080.2360.1470.0000.0580.0280.0000.0580.0000.1440.0460.0470.0350.1030.1720.0000.1940.1880.0880.0350.2480.1140.1270.2290.0300.0490.1070.0780.0360.0930.0390.0430.2470.194
3SsnPorch-0.0360.0620.0330.0320.0420.047-0.0160.0130.0490.060-0.0230.0220.034-0.019-0.0030.036-0.0280.017-0.0391.000-0.038-0.0090.0050.065-0.024-0.0510.0000.0000.0160.0730.0000.0480.0640.0000.1340.0000.0000.0000.0040.1740.0000.0000.0290.0000.2500.0000.0000.0000.0000.0580.0000.0000.0000.0000.0000.0000.0000.000
ScreenPorch-0.0220.0920.0460.0750.0390.0720.059-0.0120.0890.1080.012-0.0190.0860.0340.0320.029-0.0900.007-0.081-0.0381.0000.0190.0150.1000.0750.0490.0000.0750.0380.0000.2140.0290.0730.0450.0100.0000.0000.0710.0660.0890.0390.0740.0030.0000.0000.0310.0000.0000.0000.0000.0460.0460.0000.0250.0500.1240.0000.000
PoolArea0.0330.0840.057-0.0060.0050.0580.068-0.0370.0470.0710.0610.0660.0680.0720.0590.0420.0500.0370.004-0.0090.0191.0000.0420.058-0.012-0.0070.0000.0000.1210.0000.0000.0390.0000.0000.0370.0000.0000.0370.1290.3780.1270.1470.0310.0000.0000.0000.0580.0000.0990.0000.1060.0000.0000.0140.0000.1860.0000.000
MiscVal-0.0330.059-0.0880.087-0.0490.0050.030-0.044-0.061-0.033-0.0050.029-0.0490.013-0.021-0.0360.017-0.0350.0390.0050.0150.0421.000-0.0630.0940.0960.0000.0000.0000.0000.0000.0000.0000.0000.0000.4960.1000.0000.3510.0000.0000.0000.1490.0920.1250.0000.0560.0860.0000.0000.0000.1980.0850.0790.1170.0510.0320.089
SalePrice0.0070.4560.810-0.1290.4160.302-0.0390.1850.6030.5750.294-0.0680.7310.2350.5330.6490.3540.478-0.2180.0650.1000.058-0.0631.000-0.650-0.5770.2060.0000.1970.0960.0000.0870.0460.3190.0650.0000.0880.1290.1130.1210.1640.1750.4760.1050.2580.0810.2380.4180.1400.0490.4160.2080.0460.4620.0390.2890.4160.222
Built_to_Sale-0.040-0.105-0.6440.419-0.391-0.1850.113-0.141-0.423-0.290-0.0350.143-0.2890.031-0.177-0.525-0.286-0.3950.408-0.0240.075-0.0120.094-0.6501.0000.6930.2890.0200.1580.1600.0000.1040.1100.4690.1250.1510.2360.2750.1470.0370.3330.3250.4330.1840.5120.1770.3300.4360.1460.0820.3450.2210.1870.3980.0870.1670.3440.345
Years_Since_Remod-0.014-0.078-0.5630.052-0.229-0.0560.131-0.187-0.302-0.243-0.0800.059-0.2900.048-0.206-0.400-0.230-0.3610.236-0.0510.049-0.0070.096-0.5770.6931.0000.1930.1150.1250.1140.0870.0570.0850.3650.0900.0320.1770.2080.0910.0410.2690.2670.3760.1030.3330.0990.3280.3930.1300.0400.2580.2010.1120.4080.0730.1200.2530.183
MSZoning0.2640.0000.1900.1610.0610.0910.0000.0720.1190.1600.1590.1450.1060.1650.1750.1890.0690.1460.1470.0000.0000.0000.0000.2060.2890.1931.0000.2490.1520.1020.0000.0640.0720.6410.0710.0590.1890.1840.0730.0000.1780.1860.2390.0790.2240.0550.1170.2970.0710.0200.1750.1400.0910.1740.0000.1360.1440.218
Street0.1030.2900.0730.0680.0000.0190.0490.0000.0000.0000.0000.0000.0000.0000.0000.2600.2220.0350.0000.0000.0750.0000.0000.0000.0200.1150.2491.0000.0340.1140.0000.0000.1760.1990.1650.0000.1120.0190.0000.0000.0000.0000.3210.0000.0440.0000.0180.0400.0910.0000.0220.0000.0000.0620.0000.0590.0270.000
LotShape0.1380.2660.1160.0600.0700.2060.0560.0390.2000.2070.1480.0000.2220.0330.0900.1560.1010.0730.0580.0160.0380.1210.0000.1970.1580.1250.1520.0341.0000.1270.0000.2210.1190.2440.1050.0000.0840.0730.0350.1860.0820.0930.1120.0000.1170.0250.0530.1080.0940.0450.1020.0840.0370.0920.0000.1410.1200.075
LandContour0.0840.2560.1610.1010.0250.1380.0490.0640.1060.0960.0580.0730.1000.1100.0730.1090.1140.0000.0280.0730.0000.0000.0000.0960.1600.1140.1020.1140.1271.0000.0000.0600.4570.3600.0000.0590.0690.1260.1410.1810.1160.1210.1340.0000.1000.0000.0540.1280.1050.0270.1110.0030.0000.0970.0000.0660.0900.116
Utilities0.0000.0000.0000.0000.1700.0000.1840.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0440.0000.0000.2140.0000.0000.0000.0000.0870.0000.0000.0000.0001.0000.0850.0000.0960.0000.0000.0000.1000.0000.0000.0000.0000.0000.0000.0000.0000.0270.0000.0000.1020.0000.0000.0000.0000.0000.0000.0000.000
LotConfig0.0620.0790.0170.0000.0360.0450.0000.0120.0290.0570.0520.0000.0460.0000.0000.0420.0490.0000.0580.0480.0290.0390.0000.0870.1040.0570.0640.0000.2210.0600.0851.0000.0790.1370.1480.0920.0690.0000.0750.0770.0520.0760.0140.0000.0430.0000.0100.0630.0000.0340.0410.0000.0410.0000.0000.0490.0440.029
LandSlope0.0000.4490.1520.1880.0000.0830.1410.0530.0000.0000.0000.0530.0360.1000.0610.0400.1380.0000.0000.0640.0730.0000.0000.0460.1100.0850.0720.1760.1190.4570.0000.0791.0000.3150.0000.0000.0260.0000.2560.3130.1340.1160.0940.0000.0500.0000.0500.0000.2000.0450.1240.0410.0000.0450.0750.1540.0330.000
Neighborhood0.4220.1620.3210.2220.1820.2010.1230.1910.2370.2410.2500.1120.2090.2060.2040.2590.1940.1200.1440.0000.0450.0000.0000.3190.4690.3650.6410.1990.2440.3600.0960.1370.3151.0000.1850.0100.4190.2940.1860.0950.2880.3170.4850.1530.4170.0540.2960.3820.1890.1450.3690.3000.1020.4440.0830.3050.3910.309
Condition10.1030.0000.0610.0510.0000.1070.0000.0160.0830.0860.0520.0000.0870.0600.0770.0650.0580.0610.0460.1340.0100.0370.0000.0650.1250.0900.0710.1650.1050.0000.0000.1480.0000.1851.0000.2100.0760.0850.0810.0770.0720.0790.1250.0190.0800.0000.1570.0400.0000.0000.0620.0870.0670.0830.0000.0480.0620.103
Condition20.1030.0000.1530.0990.1030.3120.0390.0620.1440.1910.1100.0840.2710.0000.1270.1540.0000.2800.0470.0000.0000.0000.4960.0000.1510.0320.0590.0000.0000.0590.0000.0920.0000.0100.2101.0000.1440.1220.3110.0000.0230.0000.1370.2840.0350.0000.0690.0680.0000.0000.1060.1990.1220.0900.0000.0000.0000.058
BldgType0.8510.0360.1310.1280.0000.0000.0000.1220.1200.1840.1250.0760.0490.3020.1980.1410.0960.0000.0350.0000.0000.0000.1000.0880.2360.1770.1890.1120.0840.0690.0000.0690.0260.4190.0760.1441.0000.1560.0480.0270.1630.1870.1760.1100.1880.1070.1120.2890.1600.0650.1050.2290.4880.1490.0550.1250.1540.147
HouseStyle0.6170.0000.1440.1220.0460.1140.0000.1500.1640.1600.4350.2630.2580.2420.2660.1220.0520.1270.1030.0000.0710.0370.0000.1290.2750.2080.1840.0190.0730.1260.1000.0000.0000.2940.0850.1220.1561.0000.1020.0470.1600.1670.1760.1030.2160.1410.1680.2330.1650.0990.2360.4610.1500.1470.0340.0990.1640.163
RoofStyle0.1170.1120.1170.0440.1050.0960.1340.0910.1230.1500.1200.0000.0620.1400.1220.0770.0550.0110.1720.0040.0660.1290.3510.1130.1470.0910.0730.0000.0350.1410.0000.0750.2560.1860.0810.3110.0480.1021.0000.4580.1380.1600.1460.0920.0920.0000.0000.0550.1250.1270.1390.2100.1600.1110.1260.0800.1330.117
RoofMatl0.0390.2520.0990.0000.1420.4490.1540.0000.4240.3940.1060.0490.4060.1010.1300.2110.1660.1090.0000.1740.0890.3780.0000.1210.0370.0410.0000.0000.1860.1810.0000.0770.3130.0950.0770.0000.0270.0470.4581.0000.1860.1160.0660.0000.0000.0000.0000.0000.1660.1390.1060.0180.1750.0380.1530.2700.0000.000
Exterior1st0.1880.0260.2000.1890.0000.1380.0720.0940.1340.1820.1010.0000.1110.0820.0980.1420.1020.0780.1940.0000.0390.1270.0000.1640.3330.2690.1780.0000.0820.1160.0000.0520.1340.2880.0720.0230.1630.1600.1380.1861.0000.7590.3510.0950.3150.1340.2660.3490.0890.0660.2370.1200.1570.2900.0930.1450.2430.190
Exterior2nd0.2100.0710.1930.1690.0260.1390.0630.1020.1440.1550.1320.0000.1230.0670.0990.1390.1000.0810.1880.0000.0740.1470.0000.1750.3250.2670.1860.0000.0930.1210.0000.0760.1160.3170.0790.0000.1870.1670.1600.1160.7591.0000.3550.0670.3140.1740.2650.3300.0830.0680.2250.1660.1300.2840.0870.1180.2350.174
ExterQual0.2430.0000.6140.3190.2400.2080.0410.2510.3190.2710.2090.0900.2860.1710.2740.3440.1750.1790.0880.0290.0030.0310.1490.4760.4330.3760.2390.3210.1120.1340.0000.0140.0940.4850.1250.1370.1760.1760.1460.0660.3510.3551.0000.1820.3710.0420.3240.2780.0710.0430.3180.1510.0880.5460.1020.1850.3610.193
ExterCond0.1150.0000.1950.3790.0000.0000.0000.0260.0380.0000.0320.0870.0530.0000.0000.1140.0440.1600.0350.0000.0000.0000.0920.1050.1840.1030.0790.0000.0000.0000.0000.0000.0000.1530.0190.2840.1100.1030.0920.0000.0950.0670.1821.0000.1230.0460.0620.2000.0000.0590.0750.0510.0000.1780.1610.0340.1290.154
Foundation0.2630.0000.2910.2560.0750.1110.0700.1700.2330.0880.1570.0270.1520.0860.1180.1890.1380.1240.2480.2500.0000.0000.1250.2580.5120.3330.2240.0440.1170.1000.0000.0430.0500.4170.0800.0350.1880.2160.0920.0000.3150.3140.3710.1231.0000.2160.2930.3650.1030.0670.2850.1640.1670.3430.1010.1200.2700.236
Heating0.1050.0840.1670.0920.0000.0000.0000.0000.0730.0000.0430.3080.0520.0610.0360.0670.0000.0750.1140.0000.0310.0000.0000.0810.1770.0990.0550.0000.0250.0000.0000.0000.0000.0540.0000.0000.1070.1410.0000.0000.1340.1740.0420.0460.2161.0000.2390.4610.0000.0000.0000.0000.0890.1550.0610.0000.0900.147
HeatingQC0.1680.0000.2590.1780.0350.0590.0000.0980.1400.0940.1120.0590.1430.0210.0990.1410.1210.1040.1270.0000.0000.0580.0560.2380.3300.3280.1170.0180.0530.0540.0270.0100.0500.2960.1570.0690.1120.1680.0000.0000.2660.2650.3240.0620.2930.2391.0000.3790.0600.0300.1990.0970.0960.3180.0230.0970.1800.175
CentralAir0.2540.0000.3740.3150.1050.1520.0000.0600.2230.1440.0360.1260.1580.1600.1120.2760.1520.1010.2290.0000.0000.0000.0860.4180.4360.3930.2970.0400.1080.1280.0000.0630.0000.3820.0400.0680.2890.2330.0550.0000.3490.3300.2780.2000.3650.4610.3791.0000.1060.0160.1030.1300.2450.3430.0880.1960.2830.336
BsmtFullBath0.2090.2110.0660.0000.0250.3970.0880.2650.2040.1910.1250.0000.1360.2550.0630.1370.1900.0790.0300.0000.0000.0990.0000.1400.1460.1300.0710.0910.0940.1050.0000.0000.2000.1890.0000.0000.1600.1650.1250.1660.0890.0830.0710.0000.1030.0000.0600.1061.0000.0970.2640.1520.1290.0950.0000.1120.1150.086
BsmtHalfBath0.0860.0000.0620.1020.0000.0280.0880.0540.0000.0000.0000.0000.0000.0300.0000.0250.0000.0000.0490.0580.0000.0000.0000.0490.0820.0400.0200.0000.0450.0270.1020.0340.0450.1450.0000.0000.0650.0990.1270.1390.0660.0680.0430.0590.0670.0000.0300.0160.0971.0000.1640.1540.4980.0000.0000.0000.0720.016
FullBath0.2490.0980.4040.3090.1790.1580.0360.1870.2360.2580.4070.0000.4680.4480.3890.2790.2480.1660.1070.0000.0460.1060.0000.4160.3450.2580.1750.0220.1020.1110.0000.0410.1240.3690.0620.1060.1050.2360.1390.1060.2370.2250.3180.0750.2850.0000.1990.1030.2640.1641.0000.2300.1130.2780.0670.1800.3290.099
HalfBath0.5120.0000.2250.0790.1360.0120.0080.1220.0970.1210.4490.0000.3000.2500.2700.1600.0820.1490.0780.0000.0460.0000.1980.2080.2210.2010.1400.0000.0840.0030.0000.0000.0410.3000.0870.1990.2290.4610.2100.0180.1200.1660.1510.0510.1640.0000.0970.1300.1520.1540.2301.0000.1920.1480.0360.1640.1970.085
KitchenAbvGr0.4760.0000.1060.0730.0000.0000.0000.0620.0700.0410.0000.0000.0000.2330.1740.0920.0000.0000.0360.0000.0000.0000.0850.0460.1870.1120.0910.0000.0370.0000.0000.0410.0000.1020.0670.1220.4880.1500.1600.1750.1570.1300.0880.0000.1670.0890.0960.2450.1290.4980.1130.1921.0000.1020.0000.0860.1230.135
KitchenQual0.2260.0000.5400.2470.1870.2090.0400.1930.2960.2510.1830.0600.2660.1310.2380.3340.1860.1590.0930.0000.0250.0140.0790.4620.3980.4080.1740.0620.0920.0970.0000.0000.0450.4440.0830.0900.1490.1470.1110.0380.2900.2840.5460.1780.3430.1550.3180.3430.0950.0000.2780.1480.1021.0000.0850.1840.3630.191
Functional0.0830.0140.1150.1670.0000.0000.1200.0540.0000.0340.0000.0730.0000.0250.0000.0000.0760.0840.0390.0000.0500.0000.1170.0390.0870.0730.0000.0000.0000.0000.0000.0000.0750.0830.0000.0000.0550.0340.1260.1530.0930.0870.1020.1610.1010.0610.0230.0880.0000.0000.0670.0360.0000.0851.0000.0000.0420.075
Fireplaces0.1930.1600.2670.1050.1540.2980.0800.0870.3200.3410.1650.0000.3760.1070.2230.2280.1460.1220.0430.0000.1240.1860.0510.2890.1670.1200.1360.0590.1410.0660.0000.0490.1540.3050.0480.0000.1250.0990.0800.2700.1450.1180.1850.0340.1200.0000.0970.1960.1120.0000.1800.1640.0860.1840.0001.0000.2020.108
GarageCars0.2430.0110.4020.2190.2000.1760.0000.1790.2600.2380.2280.0870.2870.1340.2420.7590.1480.1290.2470.0000.0000.0000.0320.4160.3440.2530.1440.0270.1200.0900.0000.0440.0330.3910.0620.0000.1540.1640.1330.0000.2430.2350.3610.1290.2700.0900.1800.2830.1150.0720.3290.1970.1230.3630.0420.2021.0000.263
PavedDrive0.1760.0300.1750.1860.0740.1090.0000.0950.1240.1040.1120.0920.0940.0980.0950.2710.0740.0340.1940.0000.0000.0000.0890.2220.3450.1830.2180.0000.0750.1160.0000.0290.0000.3090.1030.0580.1470.1630.1170.0000.1900.1740.1930.1540.2360.1470.1750.3360.0860.0160.0990.0850.1350.1910.0750.1080.2631.000

Missing values

2023-02-02T13:33:02.542405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-02T13:33:02.890765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

MSSubClassMSZoningLotAreaStreetLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondRoofStyleRoofMatlExterior1stExterior2ndMasVnrAreaExterQualExterCondFoundationBsmtFinSF1BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAir1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesGarageCarsGarageAreaPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValSalePriceBuilt_to_SaleYears_Since_Remod
060RL8450PaveRegLvlAllPubInsideGtlCollgCrNormNorm1Fam2Story75GableCompShgVinylSdVinylSd196.0GdTAPConc7060150856GasAExY85685401710102131Gd8Typ02548Y0610000020850055
120RL9600PaveRegLvlAllPubFR2GtlVeenkerFeedrNorm1Fam1Story68GableCompShgMetalSdMetalSd0.0TATACBlock97802841262GasAExY1262001262012031TA6Typ12460Y2980000001815003131
260RL11250PaveIR1LvlAllPubInsideGtlCollgCrNormNorm1Fam2Story75GableCompShgVinylSdVinylSd162.0GdTAPConc4860434920GasAExY92086601786102131Gd6Typ12608Y0420000022350076
370RL9550PaveIR1LvlAllPubCornerGtlCrawforNormNorm1Fam2Story75GableCompShgWd SdngWd Shng0.0TATABrkTil2160540756GasAGdY96175601717101031Gd7Typ13642Y03527200001400009136
460RL14260PaveIR1LvlAllPubFR2GtlNoRidgeNormNorm1Fam2Story85GableCompShgVinylSdVinylSd350.0GdTAPConc65504901145GasAExY1145105302198102141Gd9Typ13836Y192840000025000088
550RL14115PaveIR1LvlAllPubInsideGtlMitchelNormNorm1Fam1.5Fin55GableCompShgVinylSdVinylSd0.0TATAWood732064796GasAExY79656601362101111TA5Typ02480Y40300320007001430001614
620RL10084PaveRegLvlAllPubInsideGtlSomerstNormNorm1Fam1Story85GableCompShgVinylSdVinylSd186.0GdTAPConc136903171686GasAExY1694001694102031Gd7Typ12636Y255570000030700032
760RL10382PaveIR1LvlAllPubCornerGtlNWAmesPosNNorm1Fam2Story76GableCompShgHdBoardHdBoard240.0TATACBlock859322161107GasAExY110798302090102131TA7Typ22484Y2352042280003502000003636
850RM6120PaveRegLvlAllPubInsideGtlOldTownArteryNorm1Fam1.5Fin75GableCompShgBrkFaceWd Shng0.0TATABrkTil00952952GasAGdY102275201774002022TA8Min122468Y90020500001299007758
9190RL7420PaveRegLvlAllPubCornerGtlBrkSideArteryArtery2fmCon1.5Unf56GableCompShgMetalSdMetalSd0.0TATABrkTil8510140991GasAExY1077001077101022TA5Typ21205Y04000001180006958
MSSubClassMSZoningLotAreaStreetLotShapeLandContourUtilitiesLotConfigLandSlopeNeighborhoodCondition1Condition2BldgTypeHouseStyleOverallQualOverallCondRoofStyleRoofMatlExterior1stExterior2ndMasVnrAreaExterQualExterCondFoundationBsmtFinSF1BsmtFinSF2BsmtUnfSFTotalBsmtSFHeatingHeatingQCCentralAir1stFlrSF2ndFlrSFLowQualFinSFGrLivAreaBsmtFullBathBsmtHalfBathFullBathHalfBathBedroomAbvGrKitchenAbvGrKitchenQualTotRmsAbvGrdFunctionalFireplacesGarageCarsGarageAreaPavedDriveWoodDeckSFOpenPorchSFEnclosedPorch3SsnPorchScreenPorchPoolAreaMiscValSalePriceBuilt_to_SaleYears_Since_Remod
145090RL9000PaveRegLvlAllPubFR2GtlNAmesNormNormDuplex2Story55GableCompShgVinylSdVinylSd0.0TATACBlock00896896GasATAY89689601792002242TA8Typ000Y3245000001360003535
145120RL9262PaveRegLvlAllPubInsideGtlSomerstNormNorm1Fam1Story85GableCompShgCemntBdCmentBd194.0GdTAPConc0015731573GasAExY1578001578002031Ex7Typ13840Y0360000028709010
1452180RM3675PaveRegLvlAllPubInsideGtlEdwardsNormNormTwnhsESLvl55GableCompShgVinylSdVinylSd80.0TATAPConc54700547GasAGdY1072001072101021TA5Typ02525Y0280000014500011
145320RL17217PaveRegLvlAllPubInsideGtlMitchelNormNorm1Fam1Story55GableCompShgVinylSdVinylSd0.0TATAPConc0011401140GasAExY1140001140001031TA6Typ000Y3656000008450000
145420FV7500PaveRegLvlAllPubInsideGtlSomerstNormNorm1Fam1Story75GableCompShgVinylSdVinylSd0.0GdTAPConc41008111221GasAExY1221001221102021Gd6Typ02400Y01130000018500054
145560RL7917PaveRegLvlAllPubInsideGtlGilbertNormNorm1Fam2Story65GableCompShgVinylSdVinylSd0.0TATAPConc00953953GasAExY95369401647002131TA7Typ12460Y0400000017500087
145620RL13175PaveRegLvlAllPubInsideGtlNWAmesNormNorm1Fam1Story66GableCompShgPlywoodPlywood119.0TATACBlock7901635891542GasATAY2073002073102031TA7Min122500Y3490000002100003222
145770RL9042PaveRegLvlAllPubInsideGtlCrawforNormNorm1Fam2Story79GableCompShgCemntBdCmentBd0.0ExGdStone27508771152GasAExY1188115202340002041Gd9Typ21252Y06000002500266500694
145820RL9717PaveRegLvlAllPubInsideGtlNAmesNormNorm1Fam1Story56HipCompShgMetalSdMetalSd0.0TATACBlock49102901078GasAGdY1078001078101021Gd5Typ01240Y366011200001421256014
145920RL9937PaveRegLvlAllPubInsideGtlEdwardsNormNorm1Fam1Story56GableCompShgHdBoardHdBoard0.0GdTACBlock8302901361256GasAGdY1256001256101131TA6Typ01276Y73668000001475004343